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Article Contents

What are genetically engineered crops, safety assessment of genetically engineered crops, insect-resistant crops, herbicide-tolerant crops, viral-resistant crops, genetically engineered crops on the horizon, acknowledgements, literature cited.

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Plant Genetics, Sustainable Agriculture and Global Food Security

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Pamela Ronald, Plant Genetics, Sustainable Agriculture and Global Food Security, Genetics , Volume 188, Issue 1, 1 May 2011, Pages 11–20, https://doi.org/10.1534/genetics.111.128553

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The United States and the world face serious societal challenges in the areas of food, environment, energy, and health. Historically, advances in plant genetics have provided new knowledge and technologies needed to address these challenges. Plant genetics remains a key component of global food security, peace, and prosperity for the foreseeable future. Millions of lives depend upon the extent to which crop genetic improvement can keep pace with the growing global population, changing climate, and shrinking environmental resources. While there is still much to be learned about the biology of plant–environment interactions, the fundamental technologies of plant genetic improvement, including crop genetic engineering, are in place, and are expected to play crucial roles in meeting the chronic demands of global food security. However, genetically improved seed is only part of the solution. Such seed must be integrated into ecologically based farming systems and evaluated in light of their environmental, economic, and social impacts—the three pillars of sustainable agriculture. In this review, I describe some lessons learned, over the last decade, of how genetically engineered crops have been integrated into agricultural practices around the world and discuss their current and future contribution to sustainable agricultural systems.

THE number of people on Earth is expected to increase from the current 6.7 billion to 9 billion by 2050. To accommodate the increased demand for food, world agricultural production needs to rise by 50% by 2030 ( Royal Society 2009 ). Because the amount of arable land is limited and what is left is being lost to urbanization, salinization, desertification, and environmental degradation, it no longer possible to simply open up more undeveloped land for cultivation to meet production needs. Another challenge is that water systems are under severe strain in many parts of the world. The fresh water available per person has decreased fourfold in the past 60 years ( United Nations Environmental Programme 2002 ). Of the water that is available for use, ∼70% is already used for agriculture ( Vorosmarty et al. 2000 ). Many rivers no longer flow all the way to the sea; 50% of the world's wetlands have disappeared, and major groundwater aquifers are being mined unsustainably, with water tables in parts of Mexico, India, China, and North Africa declining by as much as 1 m/year ( Somerville and Briscoe 2001 ). Thus, increased food production must largely take place on the same land area while using less water.

Compounding the challenges facing agricultural production are the predicted effects of climate change ( Lobell et al. 2008 ). As the sea level rises and glaciers melt, low-lying croplands will be submerged and river systems will experience shorter and more intense seasonal flows, as well as more flooding ( Intergovernmental Panel on Climate Change 2007 ). Yields of our most important food, feed, and fiber crops decline precipitously at temperatures much >30°, so heat and drought will increasingly limit crop production ( Schlenker and Roberts 2009 ). In addition to these environmental stresses, losses to pests and diseases are also expected to increase. Much of the losses caused by these abiotic and biotic stresses, which already result in 30–60% yield reductions globally each year, occur after the plants are fully grown: a point at which most or all of the land and water required to grow a crop has been invested ( Dhlamini et al. 2005 ). For this reason, a reduction in losses to pests, pathogens, and environmental stresses is equivalent to creating more land and more water.

Thus, an important goal for genetic improvement of agricultural crops is to adapt our existing food crops to increasing temperatures, decreased water availability in some places and flooding in others, rising salinity, and changing pathogen and insect threats ( World Bank 2007 ; Gregory et al. 2009 ; Royal Society 2009 ). Such improvements will require diverse approaches that will enhance the sustainability of our farms. These include more effective land and water use policies, integrated pest management approaches, reduction in harmful inputs, and the development of a new generation of agricultural crops tolerant of diverse stresses ( Somerville and Briscoe 2001 ).

These strategies must be evaluated in light of their environmental, economic, and social impacts—the three pillars of sustainable agriculture ( Committee on the Impact of Biotechnology on Farm - Level Economics and Sustainability and National Research Council 2010). This review discusses the current and future contribution of genetically engineered crops to sustainable agricultural systems.

Genetic engineering differs from conventional methods of genetic modification in two major ways: (1) genetic engineering introduces one or a few well-characterized genes into a plant species and (2) genetic engineering can introduce genes from any species into a plant. In contrast, most conventional methods of genetic modification used to create new varieties ( e.g. , artificial selection, forced interspecific transfer, random mutagenesis, marker-assisted selection, and grafting of two species, etc.) introduce many uncharacterized genes into the same species. Conventional modification can in some cases transfer genes between species, such as wheat and rye or barley and rye.

In 2008, the most recent year for which statistics are available, ∼30 genetically engineered crops were grown on almost 300 million acres in 25 countries (nearly the size of the state of Alaska), 15 of which were developing countries ( James 2009 ). By 2015, >120 genetically engineered crops (including potato and rice) are expected to be cultivated worldwide ( Stein and Rodriguez- Cerezo 2009 ). Half of the increase will be crops designed for domestic markets from national technology providers in Asia and Latin America.

There is broad scientific consensus that genetically engineered crops currently on the market are safe to eat. After 14 years of cultivation and a cumulative total of 2 billion acres planted, no adverse health or environmental effects have resulted from commercialization of genetically engineered crops ( Board on Agriculture and Natural Resources, Committee on Environmental Impacts Associated with Commercialization of Transgenic Plants, National Research Council and Division on Earth and Life Studies 2002 ). Both the U.S. National Research Council and the Joint Research Centre (the European Union's scientific and technical research laboratory and an integral part of the European Commission) have concluded that there is a comprehensive body of knowledge that adequately addresses the food safety issue of genetically engineered crops ( Committee on Identifying and Assessing Unintended Effects of Genetically Engineered Foods on Human Health and National Research Council 2004 ; European Commission Joint Research Centre 2008). These and other recent reports conclude that the processes of genetic engineering and conventional breeding are no different in terms of unintended consequences to human health and the environment ( European Commission Directorate - General for Research and Innovation 2010).

This is not to say that every new variety will be as benign as the crops currently on the market. This is because each new plant variety (whether it is developed through genetic engineering or conventional approaches of genetic modification) carries a risk of unintended consequences. Whereas each new genetically engineered crop variety is assessed on a case-by-case basis by three governmental agencies, conventional crops are not regulated by these agencies. Still, to date, compounds with harmful effects on humans or animals have been documented only in foods developed through conventional breeding approaches. For example, conventional breeders selected a celery variety with relatively high amounts of psoralens to deter insect predators that damage the plant. Some farm workers who harvested such celery developed a severe skin rash—an unintended consequence of this breeding strategy ( Committee on Identifying and Assessing Unintended Effects of Genetically Engineered Foods on Human Health and National Research Council 2004 ).

A truly extraordinary variety of alternatives to the chemical control of insects is available. Some are already in use and have achieved brilliant success. Others are in the stage of laboratory testing. Still others are little more than ideas in the minds of imaginative scientists, waiting for the opportunity to put them to the test. All have this in common: they are biological solutions, based on the understanding of the living organisms they seek to control and of the whole fabric of life to which these organisms belong. Specialists representing various areas of the vast field of biology are contributing—entomologists, pathologists, geneticists, physiologists, biochemists, ecologists—all pouring their knowledge and their creative inspirations into the formation of a new science of biotic controls. ( Carson 1962 , p. 278)

In the 1960s the biologist Rachel Carson brought the detrimental environmental and human health impacts resulting from overuse or misuse of some insecticides to the attention of the wider public. Even today, thousands of pesticide poisonings are reported each year (300,000 deaths globally, ∼1200 each year in California alone). This is one reason some of the first genetically engineered crops were designed to reduce reliance on sprays of broad-spectrum insecticides for pest control.

Corn and cotton have been genetically engineered to produce proteins from the soil bacteria Bacillus thuringiensis ( Bt ) that kill some key caterpillar and beetle pests of these crops. Bt toxins cause little or no harm to most nontarget organisms including beneficial insects, wildlife, and people ( Mendelsohn et al. 2003 ). Bt crops produce Bt toxins in most of their tissues. These Bt toxins kill susceptible insects when they eat Bt crops. This means that Bt crops are especially useful for controlling pests that feed inside plants and that cannot be killed readily by sprays, such as the European corn borer ( Ostrinia nubilalis ), which bores into stems, and the pink bollworm ( Pectinophora gossypiella ), which bores into bolls of cotton.

First commercialized in 1996, Bt crops are the second most widely planted type of transgenic crop. In 2009, Bt crops covered >50 million hectares worldwide ( James 2009 ). The genes encoding hundreds of Bt toxins have been sequenced ( Crickmore 2011 ). Most of the Bt toxins used in transgenic crops are called Cry toxins because they occur as crytalline proteins in nature ( Carriere et al. 2010 ; Deacon, http://www.biology.ed.ac.uk/research/groups/jdeacon/microbes/bt.htm ). More recently, some Bt crops also produce a second type of Bt toxin called a vegetative insecticidal protein ( Carriere et al. 2010 ; Crickmore 2011 ).

Bt toxins in sprayable formulations were used for insect control long before Bt crops were developed and are still used extensively by organic growers and others. The long-term history of the use of Bt sprays allowed the Environmental Protection Agency and the Food and Drug Administration to consider decades of human exposure in assessing human safety before approving Bt crops for commercial use. In addition, numerous toxicity and allergenicity tests were conducted on many different kinds of naturally occurring Bt toxins. These tests and the history of spraying Bt toxins on food crops led to the conclusion that Bt corn is as safe as its conventional counterpart and therefore would not adversely affect human and animal health or the environment ( European Food Safety Authority 2004).

Planting of Bt crops has resulted in the application of fewer pounds of chemical insecticides and thereby has provided environmental and economic benefits that are key to sustainable agricultural production. Although the benefits vary depending on the crop and pest pressure, overall, the U.S. Department of Agriculture (USDA) Economic Research Service found that insecticide use in the United States was 8% lower per planted acre for adopters of Bt corn than for non-adopters ( Fernandez- Cornejo and Caswell 2006 ). Fewer insecticide treatments, lower costs, and less insect damage led to significant profit increases when pest pressures were high ( Fernandez- Cornejo and Caswell 2006 ). When pest pressures are low, farmers may not be able to make up for the increased cost of the genetically engineered seed by increased yields. In Arizona, where an integrated pest management program for Bt cotton continues to be effective, growers reduced insecticide use by 70% and saved >$200 million from 1996 to 2008 ( Naranjo and Ellsworth 2009 ).

A recent study indicates that the economic benefits resulting from Bt corn are not limited to growers of the genetically engineered crop ( Hutchison et al. 2010 ). In 2009, Bt corn was planted on >22.2 million hectares, constituting 63% of the U.S. crop. For growers of corn in Illinois, Minnesota, and Wisconsin, cumulative benefits over 14 years are an estimated $3.2 billion. Importantly, $2.4 billion of this total benefit accrued to non- Bt corn ( Hutchison et al. 2010 ). This is because area-wide suppression of the primary pest, O. nubilalis , reduced damage to non- Bt corn. Comparable estimates for Iowa and Nebraska are $3.6 billion in total, with $1.9 billion for non- Bt corn. These data confirm the trend seen in some earlier studies indicating that communal benefits are sometimes associated with planting of Bt crops ( Carriere et al. 2003 ; Wu et al. 2008 ; Tabashnik 2010 ).

Planting of Bt crops has also supported another important goal of sustainable agriculture: increased biological diversity. An analysis of 42 field experiments indicates that nontarget invertebrates ( i.e. , insects, spiders, mites, and related species that are not pests targeted by Bt crops) were more abundant in Bt cotton and Bt corn fields than in conventional fields managed with insecticides ( Marvier et al. 2007 ). The conclusion that growing Bt crops promotes biodiversity assumes a baseline condition of insecticide treatments, which applies to 23% of corn acreage and 71% of cotton acreage in the United States in 2005 ( Marvier et al. 2007 ).

Benefits of Bt crops have also been well-documented in less-developed countries. For example, Chinese and Indian farmers growing genetically engineered cotton or rice were able to dramatically reduce their use of insecticides ( Huang et al. 2002 , 2005 ; Qaim and Zilberman 2003 ; Bennett et al. 2006 ). In a study of precommercialization use of genetically engineered rice in China, these reductions were accompanied by a decrease in insecticide-related injuries ( Huang et al. 2005 ).

Despite initial declines in insecticide use associated with Bt cotton in China, a survey of 481 Chinese households in five major cotton-producing provinces indicates that insecticide use on Bt cotton increased from 1999 to 2004, resulting in only 17% fewer sprays on Bt cotton compared with non- Bt cotton in 2004 ( Wang et al. 2008 ). A separate survey of 38 locations in six cotton-producing provinces in China showed that the number of sprays on all cotton fields dropped by ∼20% from 1996 (before widepread cultivation of Bt cotton) to 1999 (2 years after widespread cultivation of Bt cotton) ( Lu et al. 2010 ). This study also indicated a slight increase in insecticide use on all cotton fields from 1999 to 2008.

Although Bt cotton has effectively controlled its primary target pest in China (the cotton bollworm Helicoverpa armigera ), reduced use of broad-spectrum insecticides has apparently increased the abundance of some pests that are not killed by Bt cotton ( Wu et al. 2008 ; Lu et al. 2010 ). In particular, mirids, which are hemipteran insects not targeted by Bt cotton, have become more serious pests in China ( Lu et al. 2010 ). These results confirm the need to integrate Bt crops with other pest control tactics ( Tabashnik et al. 2010 ). In Arizona, such an integrated pest management (IPM) approach has been implemented ( Naranjo and Ellsworth 2009 ). In Arizona's cotton IPM system, key pests not controlled by Bt cotton are managed with limited use of narrow-spectrum insecticides that promote conservation of beneficial insects ( Naranjo and Ellsworth 2009 ). Mirids such as the Lygus bug ( Lygus hesperus ) are controlled with a feeding inhibitor, and the sweet potato whitefly ( Bemisia tabaci ) is controlled with insect growth regulators ( Naranjo and Ellsworth 2009 ).

One limitation of using any insecticide, whether it is organic, synthetic, or genetically engineered, is that insects can evolve resistance to it. For example, one crop pest, the diamondback moth ( Plutella xylostella ), has evolved resistance to Bt toxins under open field conditions. This resistance occurred in response to repeated sprays of Bt toxins to control this pest on conventional (nongenetically engineered) vegetable crops ( Tabashnik 1994 ).

Partly on the basis of the experience with the diamondback moth and because Bt crops cause a season-long exposure of target insects to Bt toxins, some scientists predicted that pest resistance to Bt crops would occur in a few years. However, global pest monitoring data suggest that Bt crops have remained effective against most pests for more than a decade ( Tabashnik et al. 2008 ; Carriere et al. 2010 ). Nonetheless, after more than a dozen years of widespread Bt crop use, resistance to Bt crops has been reported in some field populations of at least four major species of target pests ( Bagla 2010 ; Carriere et al. 2010 ; Storer et al. 2010 ).

Retrospective analyses suggest that the “refuge strategy”— i.e ., creating refuges of crop plants that do not make Bt toxins to promote survival of susceptible insects—has helped to delay evolution of pest resistance to Bt crops ( Carriere et al. 2010 ). The theory underlying the refuge strategy is that most of the rare resistant pests surviving on Bt crops will mate with abundant susceptible pests from refuges of host plants without Bt toxins. If inheritance of resistance is recessive, the hybrid offspring produced by such matings will be killed by Bt crops, markedly slowing the evolution of resistance.

In cases where resistance to Bt crops has evolved quickly, one or more conditions of the refuge strategy have not been met. For example, resistance occurred rapidly to the Bt toxin Cry1Ac in transgenic cotton in U.S. populations of Helicoverpa zea , which is consistent with the theory underlying the refuge strategy because this resistance is not recessive ( Tabashnik et al. 2008 ). In other words, the concentration of Cry1Ac in Bt cotton was not high enough to kill the hybrid offspring produced by matings between susceptible and resistant H. zea . Thus, the so-called “high dose” requirement was not met ( Tabashnik et al. 2008 ). In a related case, failure to provide adequate refuges of non- Bt cotton appears to have hastened resistance to this same type of Bt cotton by pink bollworm in India ( Bagla 2010 ). In contrast, Arizona cotton growers complied with this strategy from 1996 to 2005, and no increase in pink bollworm resistance occurred ( Tabashnik et al. 2010 ).

In the United States, Bt cotton producing only Cry1Ac is no longer registered and has been replaced primarily by Bt cotton that produces two toxins ( Carriere et al. 2010 ). More generally, most newer cultivars of Bt cotton and Bt corn produce two or more toxins. These multi-toxin Bt crops are designed to help delay resistance and to kill a broader spectrum of insect pests ( Carriere et al. 2010 ). For example, a new type of Bt corn produces five Bt toxins—three that kill caterpillars and two that kill beetles ( Dow Agrosciences 2009 ).

Despite the success of the refuge strategy in delaying insect resistance to Bt crops, this approach has limitations, including variable compliance by farmers with the requirement to plant refuges of non- Bt host plants. An alternative strategy, where refuges are scarce or absent, entails release of sterile insects to mate with resistant insects ( Tabashnik et al. 2010 ). Incorporation of this strategy in a multi-tactic eradication program in Arizona from 2006 to 2009 reduced pink bollworm abundance by >99%, while eliminating insecticide sprays against this pest. The success of such creative multidisciplinary integrated approaches, involving entomologists, geneticists, physiologists, biochemists, and ecologists, provides a roadmap for the future of agricultural production and attests to the foresight of Rachel Carson.

Weeds are a major limitation of crop production globally because they compete for nutrients and sunlight. One method to control weeds is to spray herbicides that kill them. Many of the herbicides used over the past 50 years are classified as toxic or slightly toxic to animals and humans (classes I, II, and III). Some newer herbicides, however, are considered nontoxic (class IV). An example of the latter, the herbicide glyphosate (trade name Roundup), is essentially a modified amino acid that blocks a chloroplast enzyme [called 5-enolpyruvoyl-shikimate-3-phosphate synthetase (EPSPS)] that is required for plant, but not animal, production of tryptophan. Glyphosate has a very low acute toxicity, is not carcinogenic, and breaks down quickly in the environment and thus does not persist in groundwater.

Some crop plants have been genetically engineered for tolerance to glyphosate. In these herbicide-tolerant crops, a gene, isolated from the bacterium Agrobacterium encoding an EPSPS protein resistant to glyphosate, is engineered into the plant. Growers of herbicide-tolerant crops can spray glyphosate to control weeds without harming their crop.

Although herbicide-tolerant crops do not directly benefit organic farmers, who are prohibited from using herbicides, or poor farmers in developing countries, who often cannot afford to buy the herbicides, there are clear advantages to conventional growers and to the environment in developed countries. One important environmental benefit is that the use of glyphosate has displaced the use of more toxic (classes I, II, and III) herbicides ( Fernandez- Cornejo and Caswell 2006 ). For example, in Argentina, soybean farmers using herbicide-tolerant crops were able to reduce their use of toxicity class II and III herbicides by 83–100%. In North Carolina, the pesticide leaching was 25% lower in herbicide-tolerant cotton fields compared with those having conventional cotton ( Carpenter 2010 ).

Before the advent of genetically engineered soybean, conventional soybean growers in the United States applied the more toxic herbicide, metolachlor (class III), to control weeds. Metolachlor, known to contaminate groundwater, is included in a class of herbicides with suspected toxicological problems. Switching from metolachlor to glyphosate in soybean production has had large environmental benefits and likely health benefits for farmworkers ( Fernandez- Cornejo and Mc Bride 2002 ).

In the Central Valley of California, most conventional alfalfa farmers use diuron (class III) to control weeds. Diuron, which also persists in groundwater, is toxic to aquatic invertebrates ( U.S. Environmental Protection Agency 1983 , 1988 ). Planting of herbicide-tolerant alfalfa varieties is therefore expected to improve water quality in the valley and enhance biodiversity ( Strandberg and Pederson 2002 ). The USDA Animal and Plant Health Inspection Service recently prepared a final environmental impact statement evaluating the potential environmental effects of planting this crop ( Usda Animal and Plant Health Inspection Service 2010 ).

Another benefit in terms of sustainable agriculture is that herbicide-tolerant corn and soybean have helped foster use of low-till and no-till agriculture, which leaves the fertile topsoil intact and protects it from being removed by wind or rain. Thus, no-till methods can improve water quality and reduce soil erosion. Also, because tractor tilling is minimized, less fuel is consumed and greenhouse gas emissions are reduced ( Farrell et al. 2006 ; Committee on the Impact of Biotechnology on Farm - Level Economics and Sustainability and National Research Council 2010). In Argentina and the United States, the use of herbicide-tolerant soybeans was associated with a 25–58% decrease in the number of tillage operations ( Carpenter 2010 ). Such reduced tillage practices correlate with a significant reduction in greenhouse gas emissions which, in 2005, was equivalent to removing 4 million cars from the roads ( Brookes and Barfoot 2006 ).

One drawback to the application of herbicides is that overuse of a single herbicide can lead to the evolution of weeds that are resistant to that herbicide. The evolution of resistant weeds has been documented for herbicide-tolerant traits developed through selective breeding, mutagenesis, and genetic engineering. To mitigate the evolution of weed resistance and prolong the usefulness of herbicide-tolerant crops, a sustainable management system is needed. Such approaches require switching to another herbicide or mixtures of herbicides or employing alternative weed control methods ( Committee on the Impact of Biotechnology on Farm - Level Economics and Sustainability and National Research Council 2010). Implementation of a mandatory crop diversity strategy would also greatly reduce weed resistance. Newer herbicide-tolerant varieties will have tolerance to more than one herbicide, which will allow easier herbicide rotation or mixing, and, in theory, help to improve the durability of the effectiveness of particular herbicides.

In addition to environmental issues, economic issues related to pollen flow between genetically engineered, nongenetically engineered, and organic crops and to compatible wild relatives are also important to discussions of herbicide tolerance due to possible gene flow. These issues are addressed in the USDA report on genetically engineered alfalfa and are also discussed in other reviews ( Ronald and Adamchak 2008 ; Mc Hughen and Wager 2010 ; Usda Animal and Plant Health Inspection Service 2010 ).

Although Bt and herbicide-tolerant crops are by far the largest acreage, genetically engineered crops on the market, other genetically engineered crops have also been commercialized and proven to be effective tools for sustainable agriculture. For example, in the 1950s, the entire papaya production on the Island of Oahu was decimated by papaya ringspot virus (PRSV), a potyvirus with single-stranded RNA. Because there was no way to control PRSV, farmers moved their papaya production to the island of Hawaii where the virus was not yet present. By the 1970s, however, PRSV was discovered in the town of Hilo, just 20 miles away from the papaya growing area where 95% of the state's papaya was grown. In 1992, PRSV had invaded the papaya orchards and by 1995 the disease was widespread, creating a crisis for Hawaiian papaya farmers.

In anticipation of disease spread, Dennis Gonsalves, a local Hawaiian, and co-workers initiated a genetic strategy to control the disease ( Tripathi et al. 2006 ). This research was spurred by an earlier observation that transgenic tobacco expressing the coat protein gene from tobacco mosaic virus showed a significant delay in disease symptoms caused by tobacco mosaic virus ( Powell- Abel et al. 1986 ).

Gonsalves's group engineered papaya to carry a transgene from a mild strain of PRSV. The transgene was designed with a premature stop codon in the PRSV coat protein sequence to prevent expression of a functional coat protein because, at the time of engineering, it was thought that the protein itself was an important factor in resistance. RNA analysis later revealed that the plants with the best resistance exhibited the least detectable message, which was suggestive of the involvement of an RNA silencing mechanism ( Tripathi et al. 2006 ).

Conceptually similar (although mechanistically different) to human vaccinations against polio or small pox, this treatment “immunized” the papaya plant against further infection. The genetically engineered papaya yielded 20 times more papaya than the nongenetically engineered variety after PRSV infection. By September 1999, 90% of the Hawaiian farmers had obtained genetically engineered seeds, and 76% of them had planted the seeds. After release of genetically engineered papaya to farmers, production rapidly increased from 26 million pounds in 1998 to a peak of 40 million pounds in 2001. Today, 80–90% of Hawaiian papaya is genetically engineered. There is still no conventional or organic method to control PRSV. Funded mostly by a grant from the USDA, the project cost ∼$60,000, a small sum compared to the amount the papaya industry lost between 1997 and 1998, prior to the introduction of the genetically engineered papaya.

Peer-reviewed studies of the genetically engineered crops currently on the market indicate that such crops have contributed to enhancing global agricultural sustainability. As reviewed here, benefits include massive reductions in insecticides in the environment ( Qaim and Zilberman 2003 ; Huang et al. 2005 ), improved soil quality and reduced erosion ( Committee on the Impact of Biotechnology on Farm - Level Economics and Sustainability and National Research Council 2010), prevention of the destruction of the Hawaiian papaya industry ( Tripathi et al. 2006 ), enhanced health benefits to farmers and families as a result of reduced exposure to harsh chemicals ( Huang et al. 2002 , 2005 ), economic benefits to local communities ( Qaim et al. 2010 ), enhanced biodiversity of beneficial insects ( Cattaneo et al. 2006 ), reduction in the number of pest outbreaks on neighboring farms growing nongenetically engineered crops ( Hutchison et al. 2010 ), and increased profits to farmers ( Tabashnik 2010 ). Genetically engineered crops have also dramatically increased crop yields—>30% in some farming communities ( Qaim et al. 2010 ). As has been well-documented for Bt cotton in Arizona, the ability to combine innovations in farming practice with the planting of genetically engineered seed has had a huge positive benefit/cost ratio, far beyond what could be achieved by innovating farming practices or planting genetically engineered crops alone. The benefit/cost ratio of Bt crops is the highest for any agricultural innovation in the past 100 years.

There are dozens of useful genetically engineered traits in the pipeline, including nitrogen use efficiency ( Arcadia Biosciences 2010 ). Success of crops enhanced for this efficiency would reduce water eutrophication caused by nitrogenous compounds in fertilizers and greenhouse gas emissions resulting from the energy required to chemically synthesize fertilizers.

The USDA Animal and Plant Health Inspection Service has developed a transgenic plum variety, the “HoneySweet,” which is resistant to Plum Pox, a plant disease that infects plum and other stone fruit trees, including peach, nectarine, plum, apricot, and cherries. Although Plum Pox is very rare in the United States, and its outbreaks are immediately eradicated, the HoneySweet variety was developed as a precautionary measure to avoid a major disruption in the availability of plums, prunes, and other stone fruits should Plum Pox become widespread as is already the case in Europe Usda Animal and Plant Health Inspection Service 2009 ).

Other promising applications of genetic engineering are those that affect staple food crops. For example, rice is grown in >114 countries on six of the seven continents. In countries where rice is the staple food, it is frequently the basic ingredient of every meal. Thus, even modest changes in tolerance to environmental stress or enhanced nutrition in rice can have a large impact in the lives of the poor.

With regard to nutritional enhancements, some efforts have focused on vitamin deficiencies. Vitamin A deficiency is a public health problem in >100 countries, especially in Africa and Southeast Asia, affecting young children and pregnant women the most ( Golden Rice Project 2010 ). Worldwide, >124 million children are estimated to be vitamin A-deficient. Many of these children go blind or become ill from diarrhea, and nearly 8 million preschool-age children die each year as the result of this deficiency. Researchers estimate that 6000 children and young mothers die every day from vitamin A deficiency-related problems ( Potrykus 2010 ). The World Health Organization estimates that improved vitamin A nutritional status could prevent the deaths of 1.3–2.5 million late-infancy and preschool-age children each year ( Humphrey et al. 1992 ).

To combat vitamin A deficiency, the World Health Organization has proposed an arsenal of nutritional “well-being weapons,” including a combination of breastfeeding and vitamin A supplementation, coupled with long-term solutions, such as promoting vitamin A-rich diets and food fortification. In response to this challenge, a group of Rockefeller Foundation-supported scientists decided to try to fortify rice plants with higher levels of carotenoids, which are precursors to vitamin A. Using genetic engineering, they introduced a gene from daffodils (which make carotenoids, the pigment that gives the flower its yellow color) and two genes from a bacterium into rice ( Ye et al. 2000 ). The resulting geneticially engineered golden and carotenoid-rich rice plants were named “Golden Rice.”

Results from human feeding studies indicate that the carotenoids in the second generation of Golden Rice (called Golden Rice-2) can be properly metabolized into the vitamin A that is needed by children ( Tang et al. 2009 ). One 8-ounce cup of cooked Golden Rice-2 provides ∼450 μg of retinol, which is equivalent to 50–60% of the adult Recommended Dietary Allowance of vitamin A. Other studies support the idea that widespread consumption of Golden Rice would reduce vitamin A deficiency, saving thousands of lives ( Stein et al. 2006 ). The positive effects of Golden Rice are predicted to be most pronounced in the lowest income groups at a fraction of the cost of the current supplementation programs ( Stein et al. 2006 , 2008 ). If predictions prove accurate, this relatively low-tech, sustainable, publicly funded, people-centered effort will complement other approaches, such as the development of home gardens with vitamin A-rich crops, such as carrots and pumpkins.

In a sense, the resulting nutritionally enhanced rice is similar to vitamin D-enriched milk—except the process is different. Vitamin A fortification of rice is also similar to adding iodine to salt, a process credited with drastically reducing iodine-deficiency disorders in infants. Worldwide, iodine deficiency affects ∼2 billion people and is the leading preventable cause of mental retardation. The benefits of iodized salt are particularly apparent in Kazakhstan where local food supplies seldom contain sufficient iodine and where fortified salt was initially viewed with suspicion. Campaigns by the government and nonprofit organizations to educate the public about fortified salt required both money and political leadership, but they eventually succeeded. Today, 94% of households in Kazakhstan use iodized salt, and the United Nations is expected to certify the country officially free of iodine-deficiency disorders ( Ronald and Adamchak 2008 ).

The development of genetically engineered crops that are tolerant of environmental stresses is also predicted to be broadly beneficial. Such crops are expected to enhance local food security, an issue of importance especially for farmers in poorer nations that have limited access to markets and are now often dependent on others for their staple foods ( Royal Society 2009 ).

The development of submergence tolerant rice (Sub1 rice), through a nongenetically engineered process that involved gene cloning and precision breeding, demonstrates the power of genetics to improve tolerance to environmental stresses such as flooding, which is a major constraint to rice production in South and Southeast Asia ( Xu et al. 2006 ). In Bangladesh and India, 4 million tons of rice, enough to feed 30 million people, are lost each year to flooding. Planting of Sub1 rice has resulted in three- to fourfold yield increases in farmers’ fields during floods compared to conventional varieties. Although the Sub1 rice varieties provided an excellent immediate solution for most of the submergence-prone areas, a higher and wider range of tolerance is required for severe conditions and longer periods of flooding. With increasing global warming, unusually heavy rainfall patterns are predicted for rain-fed as well as irrigated agricultural systems. For these reasons, we and others have identified additional genes that improve tolerance ( Seo et al. 2011 ). Such genes may be useful for the development of “Sub1 plus ” varieties.

In Africa, three-quarters of the world's severe droughts have occurred over the past 10 years. The introduction of genetically engineered drought-tolerant corn, the most important African staple food crop, is predicted to dramatically increase yields for poor farmers ( African Agricultural Technology Foundation 2010 ). Drought-tolerant corn will be broadly beneficial across almost any non-irrigated agricultural situation and in any management system. Drought-tolerance technologies are likely to benefit other agricultural crops for both developed and developing countries.

In addition to environmental stresses, plant diseases also threaten global agricultural production ( Borlaug 2008 ). For example, an epidemic of stem rust threatens wheat, a crop that provides 20% of the food calories for the world's people. Because fungal spores travel in the wind, the infection spreads quickly. Stem rust has caused major famines since the beginning of history. In North America, huge grain losses occurred in 1903 and 1905 and from 1950 to 1954. During the 1950s, Norman Borlaug and other scientists developed high-yielding wheat varieties that were resistant to stem rust and other diseases. These improved seeds not only enabled farmers around the world to hold stem rust at bay for >50 years but also allowed for greater and more dependable yields. However, new strains of stem rust, called Ug99 because they were discovered in Uganda in 1999, are much more dangerous than those that destroyed as much as 20% of the American wheat crop 50 years ago. Effective resistance does not exist in American wheat and barley varieties, but recently resistance was identified in African varieties and molecular markers mapped to facilitate introgression of the trait using marker-assisted selection ( Steffenson 2011 ).

Bananas and plantains are the world's fourth most important food crop after rice, wheat, and maize. Approximately one-third of the bananas produced globally are grown in sub-Saharan Africa, where the crop provides >25% of the food energy requirements for >100 million people in East Africa alone. Banana Xanthomonas wilt disease, caused by the Gram-negative bacterium Xanthomonas vasicola pv. musacearum , is a major threat to banana productivity in eastern Africa ( Tripathi et al. 2009 ; Studholme et al. 2010 ). Cavendish banana, which represents 99% of export bananas, is threatened by a virulent form of the soil-borne fungus Fusarium oxysporum called Tropical Race Four ( Peed 2011 ). The fungal leaf spot disease Black Sigatoka, caused by the ascomycete Mycosphaerella fijiensis , has spread to banana plantations throughout the tropics and is increasingly resistant to chemical control ( Marin et al. 2003 ). Research to develop new methods to control these diseases of banana are underway in several laboratories.

For hundreds of years, farmers have relied on genetically improved seed to enhance agricultural production. Without the development of high-yielding crop varieties over recent decades, two to four times more land would have been needed in the United States, China, and India to produce the same amount of food. Looking ahead, without additional yield increases, maintaining current per capita food consumption will necessitate a near doubling of the world's cropland area by 2050. By comparison, raising global average yields to those currently achieved in North America could result in a very considerable sparing of land ( Waggoner 1995 ; Green et al. 2005 ). Because substantial greenhouse gases are emitted from agricultural systems, and because the net effect of higher yields is a dramatic reduction in carbon emissions ( Burney et al. 2010 ), development and deployment of high-yielding varieties will be a critical component of a future sustainable agriculture.

Thus, a key challenge is to raise global yields without further eroding the environment. Recent reports on food security emphasize the gains that can be made by bringing existing agronomic and food science technology and know-how to people who do not yet have it. These reports also highlight the need to explore the genetic variability in our existing food crops and to develop new genetic approaches that can be used to enhance more ecologically sound farming practices ( Naylor et al. 2007 ; World Bank 2007 ; Royal Society 2009 ).

Despite the demonstrated importance of genetically improved seed, there are still agricultural problems that cannot be solved by improved seed alone, even in combination with innovative farming practices. A premise basic to almost every agricultural system (conventional, organic, and everything in between) is that seed can take us only so far. Ecologically based farming practices used to cultivate the seed, as well as other technological changes and modified government policies, clearly are also required.

In many parts of the world, such policies involve building local educational, technical, and research capacity, food processing capability, storage capacity, and other aspects of agribusiness, as well as rural transportation and water and communications infrastructure. The many trade, subsidy, intellectual property, and regulatory issues that interfere with trade and inhibit the use of technology must also be addressed to assure adequate food availability to all. Despite the complexity of many of these interrelated issues, it is hard to avoid the conclusion that ecological‐farming practices using genetically engineered seed will play an increasingly important role in a future sustainable agriculture.

Fourteen years of extensive field studies ( Carpenter 2010 ) have demonstrated that genetically engineered crops are tools that, when integrated with optimal management practices, help make food production more sustainable. The vast benefits accrued to farmers, the environment, and consumers explain the widespread popularity of the technology in many regions of the world. The path toward a future sustainable agriculture lies in harnessing the best of all agricultural technologies, including the use of genetically engineered seed, within the framework of ecological farming.

I am grateful to Peggy Lemaux, Kent Bradford, and Bruce Tabshnik for helpful discussions and critical review of the manuscript. This work was supported by National Institutes of Health grant GM055962 and the Department of Energy, Office of Science, Office of Biological and Environmental Research, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the Department of Energy.

Communicating editor: J. Rine

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Diamonds in the not-so-rough: Wild relative diversity hidden in crop genomes

Roles Conceptualization, Writing – original draft, Writing – review & editing

Affiliation Plant Genetics Research Unit, United States Department of Agriculture, Agricultural Research Service, Columbia, Missouri, United States of America

Roles Writing – original draft

Affiliation Department of Plant Sciences, University of California, Davis, California, United States of America

Roles Conceptualization, Writing – original draft

Affiliation The Global Crop Diversity Trust, Bonn, Germany

Roles Conceptualization, Writing – review & editing

Affiliation Department of Agronomy and Plant Genetics, University of Minnesota, Minneapolis, Minnesota, United States of America

* E-mail: [email protected]

Affiliation Department of Evolution and Ecology, Center for Population Biology, and Genome Center, University of California, Davis, California, United States of America

• Sherry Flint-Garcia, 
• Mitchell J. Feldmann, 
• Hannes Dempewolf, 
• Peter L. Morrell, 
• Jeffrey Ross-Ibarra

Published: July 13, 2023

• https://doi.org/10.1371/journal.pbio.3002235
• Reader Comments

Crop production is becoming an increasing challenge as the global population grows and the climate changes. Modern cultivated crop species are selected for productivity under optimal growth environments and have often lost genetic variants that could allow them to adapt to diverse, and now rapidly changing, environments. These genetic variants are often present in their closest wild relatives, but so are less desirable traits. How to preserve and effectively utilize the rich genetic resources that crop wild relatives offer while avoiding detrimental variants and maladaptive genetic contributions is a central challenge for ongoing crop improvement. This Essay explores this challenge and potential paths that could lead to a solution.

Citation: Flint-Garcia S, Feldmann MJ, Dempewolf H, Morrell PL, Ross-Ibarra J (2023) Diamonds in the not-so-rough: Wild relative diversity hidden in crop genomes. PLoS Biol 21(7): e3002235. https://doi.org/10.1371/journal.pbio.3002235

Academic Editor: Pamela C. Ronald, University of California, Davis, UNITED STATES

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Funding: We wish to acknowledge funding the National Science Foundation (NSF IOS #1546719 to J.R.I. and S.F.G and USDA Hatch project CA-D-PLS-2066-H to J.R.I. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Plant domestication is a process that began with cultures around the world experimenting with alternative means of food production. These experiments have expanded, undoubtedly, beyond the imaginations of their originators. Much of the planet’s surface is now involved, and most of human sustenance derives from experiments that originated among fewer than 1,000,000 humans starting 10 to 12,000 years before present. Domestication has involved a mix of intentional (or “artificial”) selection and unintentional (sometimes “unconscious”) selection—the simple result of differential survival and reproduction among individuals in a novel environment [ 1 ].

Changes resulting from domestication occurred slowly. Archeological evidence demonstrates protracted, gradual change that lasted thousands of generations for many domesticates ( Box 1 ) [ 2 ]. Evolution continues today in traditional populations outside of formal breeding programs [ 3 ]. While there are clear examples of genetic loci that have a major impact on domestication phenotypes, in each cultivated species the process likely involved changes in hundreds or thousands of genes [ 4 – 7 ]. This results in a continuum of morphological and genetic differentiation, but crops and their relatives can nonetheless be usefully categorized into 3 broad groups ( Fig 1 ). The first are extant populations of wild plants that share a common (wild) ancestor with domesticates some time in the past. These extant populations are not the direct progenitors of crops but can be identified as crop wild relatives. They include taxa most closely related to a domesticate, as well as more distantly related taxa, especially those that can hybridize with the domesticate. Second are domesticated plants that result from intentional and unintentional selection by indigenous peoples, known as traditional varieties (often called landraces). These are often diverse and continue to be cultivated and selected in smaller-scale agricultural settings worldwide. Third are modern cultivars, which have been developed in the past century from directed breeding efforts following the advent of industrial agriculture. Modern cultivars are typically highly adapted to current agronomic environments and display desired characteristics often absent from crop wild relatives and traditional varieties, such as high, stable yields or ease of processing and transportation.

Box 1. Glossary

Domesticates.

Plants that have coevolved with humans. Most domesticates rely on humans for survival and reproduction.

Deleterious alleles

Alleles that decrease the survival or reproductive capacity of an organism.

Genetic drift

One of the fundamental evolutionary processes, genetic drift refers to stochastic changes in allele frequencies unrelated to an allele’s impact on fitness.

Purifying selection

Natural selection that removes deleterious alleles from a population.

Genetic resources related to the species being studied, including wild relatives, unimproved populations such as landraces or heirlooms, and improved varieties. These genetic resources—usually seeds but possibly including living plants or tissue culture—are collected and maintained for long-term preservation and are commonly used in genetic studies or breeding programs.

Introgression

Introduction of genetic material from one taxa or species into another. Introgression may occur naturally via hybridization or via inbreeding by traditional crossing and repeated backcrossing to a recurrent parent. The size of the introgressed region depends on the local recombination rate and how many backcrosses have occurred; each backcross with the recurrent parent results in reduction of the donor genome by approximately 50%.

Elite recurrent parent

The recipient parent of an introgression, typically an improved variety with high yield or superior quality, that is otherwise lacking a particular trait to be introduced from a donor variety.

Heterotic patterns

In species that exhibit heterosis or hybrid vigor, there are often specific germplasm combinations that result in higher or lower levels of heterosis. Low levels of heterosis may result when crossing individuals from the same heterotic group, while crosses between individuals from 2 different heterotic groups result in higher levels of heterosis.

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Modern cultivars of perennial crops like strawberry and citrus have often undergone relatively few generations of selection from a common ancestor with their wild relatives compared with many annual cereal crops, and modern cultivars of many perennial species may have resulted from hybridization among wild taxa or earlier varieties. For annual crops like maize, domestication involved an extended process of hundreds or thousands of generations of selection resulting in traditional varieties. Traditional varieties regularly exchange genes with crop wild relatives and are shaped by continual selection imposed by farmers and adaptation to their environment. Adaptation to modern agricultural conditions, here identified as crop improvement, is a relatively recent process usually involving only tens of generations of selection. Figure was created using BioRender.com .

https://doi.org/10.1371/journal.pbio.3002235.g001

The evolutionary path from wild species to domesticate is different for each crop ( Fig 1 ); thus, the demographic and genetic history of each species is unique. Annual species typically experienced greater reductions in diversity and more generations of strong selection than perennial crops; perennials often retain more diversity but also more deleterious alleles ( Box 1 ) or genetic load [ 8 , 9 ]. Long-lived perennial and clonally propagated species may have undergone fewer generations of differentiation from their wild progenitors, and modern cultivars may thus differ little from traditional varieties. For example, only a handful of generations and a few genetic crosses separate citrus or strawberry modern cultivars from their wild progenitors [ 6 , 10 ]. Differences in plant mating systems likely affected opportunities for gene flow, and the structuring of genetic diversity across populations [ 11 ] and preexisting ecological relationships may have preadapted some species to more rapid domestication [ 12 ]. In addition to these biological factors, historical contingencies may have played a significant role in the evolution of many crops [ 13 ], including whether domestication happened once, as in maize [ 14 ], or multiple times, as in barley [ 15 ] and amaranth [ 16 ].

Whether domestication entailed thousands of generations of gradual selection or the extraction of a single clonal genotype from wild populations, it nearly always results in the loss of genetic diversity in traditional varieties and modern cultivars compared with crop wild relatives. An understanding of the extent of loss in genetic diversity in domesticates is relatively new [ 17 ], as earlier natural history often emphasized the diversity of phenotypic forms in cultivated varieties [ 18 , 19 ]. But molecular markers reveal that crop diversity largely represents a subset of that in wild relatives [ 20 ], and a loss of diversity is also evident in comparisons of the genetic variation underlying agronomic phenotypes [ 21 ]. The initial stages of domestication almost invariably involved only a subset of crop wild relative individuals, and much of the loss of diversity likely resulted from this sampling process and genetic drift ( Box 1 ) [ 22 ]. But allelic diversity is also lost by positive selection fixing alleles relevant for domestication and purifying selection ( Box 1 ) removing deleterious alleles ( Fig 2 ). Modern breeding accentuates both drift and selection, resulting in an ever-narrowing base of diversity available for further improvement [ 23 , 24 ]. Indeed, while perennial crops diverge from their wild relatives by fewer generations and thus may better capture their wild relative genotypes, diversity in perennial modern cultivars is often low because only a small number of varieties are in widespread use [ 25 , 26 ].

Shown are 3 chromosomes sampled from populations of crop wild relatives, traditional varieties, and modern cultivars. Selection during crop evolution increases the frequency of domestication-related alleles, which are beneficial in agronomic settings, but not necessarily alleles for adaptation, which may only be beneficial in specific environments. Deleterious alleles are often concentrated in low recombination regions of the genome (white on the recombination scale bar) and preferentially removed by purifying selection, but some are fixed during the process of domestication and improvement. Adaptive alleles linked to deleterious alleles are difficult to introgress because of their negative impacts on fitness or agronomic traits (red dashed line labeled “hard”), but adaptive alleles far from deleterious alleles can be easily introgressed (red dashed arrow labeled “easy”). But the long-term combined action of introgression, recombination, and selection has allowed the historical introgression of “hard” adaptation alleles from crop wild relatives into traditional varieties (gray dashed arrow), where they could then be more easily incorporated into modern cultivars. Figure was created using BioRender.com .

https://doi.org/10.1371/journal.pbio.3002235.g002

In this Essay, we argue that allelic diversity from crop wild relatives likely already exists in cultivated populations conserved in germplasm ( Box 1 ) repositories. These alleles have been tested by evolution in an agronomic background. Many are of sufficient age that the 2-fold sieve of recombination and selection have separated them from linked deleterious variants. Surveying domesticated traditional varieties for functionally relevant variation from crop wild relatives may thus greatly facilitate the identification and incorporation of useful wild diversity into modern breeding programs.

Using crop wild relatives directly in breeding programs

Crop wild relatives contain a wealth of alleles that were lost during domestication and improvement [ 27 – 29 ]. These alleles can be valuable sources of desirable traits such as disease and insect resistance, abiotic stress resistance, flavor and nutritional quality, and plant growth and health. Incorporating this diversity can improve breeding populations, support emerging markets and novel products, and contribute to adapting crops to changing climates [ 29 – 31 ]. For any given crop, there are often multiple crop wild relatives that could potentially be useful and may vary in terms of genetic distance, interfertility, and maintenance of useful traits relative to the relevant modern cultivar. Prioritizing which samples and populations are maintained in collections, evaluated for desirable traits, and incorporated into modern cultivars is both necessary and challenging.

Crop wild relatives and traditional varieties are a good source of novel variation

Crop wild relatives and traditional varieties share much of their genetic makeup with modern cultivars. In the initial stages of cultivation and in incipient domesticates, long segments of the genome will be shared with wild relatives. These relationships reflect identity by descent (IBD) from parents to progeny. The size of IBD regions is reduced each generation by recombination and is dependent on factors such as the outcrossing rate and diversity within populations. Individuals from closely related populations can share large regions of IBD over hundreds of generations [ 32 ]. Sharing of large regions of IBD is also a hallmark of recent introgression ( Box 1 ) and distinguishing between shared ancestry and recent introgression can be difficult.

Because domestication is a recent evolutionary process, the majority of gene-level variants in modern cultivars, including single-nucleotide changes and insertions and deletions, are a subset of those found in crop wild relatives (c.f. [ 33 , 34 ]). Nonetheless, modern cultivars have also diverged as a result of genetic drift, selection, and the accumulation of new mutations. These factors are particularly important in clonally propagated species where recombination is largely absent [ 8 ]. Variants are also arranged into new haplotypes by recombination. This can include multiple combinations of functional variants such as amino acid changing mutations and regulatory elements.

Direct introgression of crop wild relative alleles is a major strategy for increasing genetic diversity and genetic variation in commercial breeding programs. Breeders typically use introgression to respond to an emerging threat or existing deficiency in the commercial germplasm collection. Genetic variation is required for breeders to make genetic gains, and alleles that can confer a selective advantage for emerging threats such as a new disease or climatic extremes may not exist in modern cultivars. In this case, researchers and breeders look for the desired trait variation in crop wild relatives in hopes of finding individuals that contain alleles with large genetic effects capable of producing adapted progeny. This approach has been successful in many cases [ 35 ], including for disease resistance [ 36 , 37 ] and abiotic stress [ 38 ]. Even with genes of large effect for domestication or improvement where beneficial alleles have been fixed in modern cultivars, agronomically relevant variation may exist in crop wild relatives. For example, branching was selected against during sunflower domestication but was later reintroduced from a wild relative to facilitate hybrid breeding [ 39 ]. Selection at domestication or improvement loci often results in the fixation of one or a few haplotypes, sometimes inadvertently fixing inferior alleles at nearby linked genes [ 40 , 41 ]. Even at loci directly targeted by selection during breeding, crop wild relatives often exhibit diverse allelic series [ 42 , 43 ], including variation at loci conferring favorable variation for yield components [ 44 , 45 ].

Incorporating novel diversity from crop wild relatives into commercial breeding populations is the fundamental goal of “pre-breeding” for many crops. The pre-breeding process requires phenotyping crop wild relatives, identifying key lineages (donors) with beneficial or novel alleles, and introgressing the donor alleles. Introgression is most commonly done by crossing diverse germplasm with the relevant modern cultivar in conjunction with selection for the novel trait or allele using phenotype, genetic markers, or predicted breeding values [ 46 ]. One successful example of pre-breeding is the Germplasm Enhancement of Maize, a coordinated effort of the US Department of Agriculture, university breeders, and industry partners to widen the germplasm base of commercial hybrid corn in the United States through the incorporation of traditional variety alleles in elite modern cultivars [ 47 ]. Some sectors of private industry also invest in efforts such as “discovery breeding,” where broader germplasm, including traditional varieties and crop wild relatives, are explored to improve modern cultivars.

Crop wild relatives are replete with maladaptive alleles

By definition, crop wild relatives are unimproved or less improved than modern cultivars and hence harbor alleles that are maladaptive under modern agronomic practices [ 48 ]. These maladaptations include photoperiod sensitivity, plant architectures less amenable to harvest, or susceptibility to biotic and abiotic stresses, all of which can dramatically affect yield and product quality (e.g., [ 29 , 49 ]). In addition, crop wild relatives are often not suited for the long-term storage or long-distance transportation systems of modern food supply chains. For these reasons, they are usually not used directly in breeding programs and are instead subjected to the pre-breeding process described above. Following hybridization of the crop wild relative donor with an elite recurrent parent ( Box 1 ), each backcross with the recurrent parent results in a loss of half of the existing donor genome. For example, after 5 generations of backcrossing, the expected proportion of the donor parent is only 3.125%. Recombination during this process is relatively limited, however, resulting in the introgression of the large chromosomal regions surrounding a target locus. When selecting for a single locus, backcrossing will also often result in the introgression of off-target regions elsewhere in the genome. This contributes to a form of linked selection [ 50 ] known as linkage drag and frequently results in a decrease in agronomic performance as genes that are not the direct targets of selection tend to carry alleles that are detrimental in a modern cultivar background. Genome-scale genetic data have revealed evidence of linkage drag in many crops [ 51 , 52 ], and recent analyses in sunflower showed not only that linkage drag results in decreasing yield, but also that introgressions from more distantly related species are more deleterious than those from closely related taxa [ 53 ].

One solution to linkage drag is marker-assisted backcrossing, where flanking markers are used to track the desired allele in a breeding population and to make selections, and genome-wide markers are used to actively select against the remaining donor genome and for the recurrent parent, effectively prioritizing recombination events close to the target locus. The added expense and effort of implementing marker-assisted backcrossing in a breeding program is such that it essentially requires an allele with a large effect to recover the value. In addition, the result is unlikely to be a single gene introgression; larger introgressions can contain dozens to hundreds of genes depending on the genomic context. Each desirable allele will reside in a region of the genome that may contain numerous maladapted alleles, and the combination of recombination rate and haplotype structure (whether beneficial and deleterious alleles are on the same or different haplotypes) will determine the likelihood of breaking up linkage blocks ( Fig 2 ). In the end, the merits and consequences of introgression will depend on several factors, including variation present in the breeding program and the number and effect size of the loci underlying the trait to be introgressed [ 54 ].

Overall, while the long-term advantages of increasing diversity and adding functional variation from crop wild relatives are well understood, the short-term challenges are often sufficient to prevent the effective utilization of such wild relatives in breeding programs. This is especially true in industry settings focused on short-term profits (though there are some notable exceptions). The commitment to existing heterotic patterns ( Box 1 ) makes wide crosses with wild relatives even less palatable for hybrid crops. The combination of linkage drag, logistical challenges with backcrossing and marker-assisted selection, and the time scale involved (many years) make the effort required to introgress crop wild relative alleles often not worth the gain. Exceptions to this tend to be large-effect loci where significant agronomic gains are clear [ 29 ].

Crop wild relative alleles have actively introgressed into traditional variety germplasm

Historically, domestication has often been portrayed as the split between cultivated plants and their crop wild relatives. However, empirical studies from a variety of systems highlight that domestication was a complex process that unfolded across a diverse landscape and involved genetic exchange both with a crop’s direct progenitor, as well as with additional wild relatives [ 55 , 56 ]. Human dissemination of crops from centers of origin often happened relatively quickly; crops in the Fertile Crescent, for example, are estimated to have spread from their center of origin at a rate of 1 km/yr [ 57 ] and maize spread from the lowlands of Mexico to the Andes in South America in less than 3,000 years [ 58 ]. This rapid diffusion forced crops to quickly adapt to new growing environments but also provided the opportunity for hybridization with locally adapted wild relatives. For example, in wheat and other complex polyploid plants, hybridization with wild relatives was essential to the formation of modern cultivated forms [ 59 ]. In scarlet runner bean, a complex history of introgression from wild relatives spans both ancient and recent crop evolution [ 60 ]. And in numerous crops such as avocado [ 61 ], citrus [ 62 ], and apple [ 63 ], modern varieties are the result of complex patterns of introgression from one or more wild relatives. Indeed, evidence suggests the vast majority of food crops actively hybridize with wild relatives in some part of their range [ 64 ].

Far from being accidental or detrimental, gene flow with crop wild relatives has often been instrumental in the evolution of domesticated taxa. In maize, for example, a meaningful subset of recent selection in traditional varieties has been for alleles introgressed from a wild relative [ 65 ], and introgression from a different wild relative contributed to highland adaptation [ 66 ]. This may have led to superior varieties that replaced preexisting domesticated populations across the Americas [ 67 ]. As genome-scale investigation of domesticates and crop wild relatives has expanded, researchers are increasingly identifying examples of adaptive introgression from wild relatives contributing to local adaptation in crops as diverse as barley [ 68 ] and date palms [ 69 ]. In some cases, even the adaptive locus itself can be identified [ 62 , 67 , 70 ]. Indeed, traditional farmers across the globe will often tolerate wild relatives in or near their fields, sometimes actively encouraging hybridization with the crop [ 71 ], with the idea that such introgression makes their crop “stronger” [ 72 ]. Perhaps the best example of this is tomato, where early farmers and breeders have brought in a host of traits from wild relatives including disease resistance [ 73 ].

If introgression from crop wild relatives generally increases maladaptation due to linkage drag and deleterious alleles, why have these processes not prevented historical gene flow? In fact, it is likely that hybridization with crop wild relatives was constrained by maladaptation. For example, ongoing gene flow between traditional varieties of maize and one of its wild relatives is depleted around loci important for maize domestication [ 74 ]. But this constraint varies across the genome; in some genomic regions, alleles from crop wild relatives may mitigate genetic load inadvertently fixed during domestication [ 53 , 75 – 78 ]. More importantly, much of the introgression between crop wild relatives and traditional varieties occurred many generations in the past and involved traditional variety populations much larger than modern breeding pools. Combined, these factors maximize the effect of recombination in breaking up linkage between beneficial alleles and maladaptive alleles at linked sites; for example, recent characterization of introgression from a wild relative in maize found the majority of introgressed segments to be quite small, often including only a single gene [ 67 ]. Large population sizes and long time periods also mean that selection by farmers—both intentional and unintentional—has had considerable opportunity to remove introgressed haplotypes with maladaptive alleles.

Future prospects for crop wild relatives in germplasm improvement

How can wild relatives and existing germplasm resources be best used to adapt crops to the novel environments and agronomic practices that will accompany changing climates? One approach that has garnered much public attention is the use of novel genome editing techniques to “domesticate” wild plants or introduce alleles from crop wild relatives into modern germplasm. In one recent study, researchers edited a number of key genes to dramatically change the architecture and agronomic suitability of a wild relative of tomatillo [ 79 ]. In another, researchers demonstrated the feasibility and potential yield gain of introducing an allele identified in a wild relative into elite hybrid maize germplasm [ 80 ]. By segregating edits away from the initial transgenes, these approaches could circumvent regulations and concerns about genetically modified organisms. Genome editing also avoids the potential for linkage drag of deleterious alleles from crop wild relatives linked to the locus of interest.

However, we would argue that such approaches are not likely to be the most fruitful avenue for using wild relative diversity to improve crops. Domestication invariably involves changes at hundreds or thousands of alleles [ 56 ], such that it is unlikely that a crop wild relative “domesticated” via genome editing will be comparable in yield or other characteristics to modern cultivars [ 81 ]. Genetic engineering approaches also suffer from a number of logistic and scientific disadvantages [ 48 ]. First, such approaches require sufficient a priori genetic knowledge to identify the causative allele. Although causative alleles have been identified for a handful of traits in some species, the vast majority of functionally relevant diversity in most species remains entirely uncharacterized. Second, not all species are amenable to tissue culture or transformation, and within many taxa not all individuals are amenable to these practices. In maize, for example, while some private sector companies have been able to edit many varieties, public breeding and research efforts are still mostly restricted to using a small number of older inbred plants that can be readily transformed but are considered genetically inferior. This limitation means novel edited alleles still need to be backcrossed into the relevant germplasm, which carries the risk of linkage drag. Third, it is often unclear how novel edits or transgenes will behave in a new genetic background, potentially leading to undesirable epistatic interactions. Given these challenges, as well as the time, cost, and effort, only alleles with large genetic effects are generally considered for editing.

While genome editing is undoubtedly a useful tool, we argue that an effective and efficient avenue for incorporating crop wild relative diversity into modern germplasm is to use wild relative alleles already present in traditional varieties housed in germplasm banks. Germplasm repositories maintain a wealth of historical and modern genetic diversity. They increasingly include additional genetic and phenotypic data that can be used a priori to help narrow down useful material [ 82 – 84 ]. Increasing evidence of gene flow between traditional varieties and wild relatives during crop evolution means that germplasm collections of traditional varieties likely harbor a wealth of untapped diversity from wild relatives. Importantly, these alleles have already been filtered by the combined action of recombination and both intentional and unintentional selection ( Fig 2 ). Crop wild relative alleles surviving in traditional varieties at appreciable frequency are thus unlikely to be linked to strongly deleterious variation and are likely to work reasonably well in a domesticated genetic background. Because these alleles are already present in a traditional variety, evaluation and later introgression into elite material is substantially easier than working directly with the wild relative. Finally, use of such materials circumvents the need to know causal alleles or mechanisms; coupled with evaluation or genomic selection, crossing with traditional varieties can effectively introgress many alleles at unknown loci across the genome without the need to understand precise causal mechanisms [ 85 ].

The incorporation of crop wild relative and traditional variety alleles into elite breeding programs is dependent on a number of factors, including the complexity of the trait or traits of interest, the ease of intercrossing, generation time, and the timeline for trait development. At one extreme, some crops may require lengthy pre-breeding interventions involving multiple crosses to bridge between diverse germplasm and relevant modern cultivars. Such bridging crosses limit the potential for genetic exchange and slow the process of introgression. But in species with genetic compatibility among wild relatives or traditional varieties, the development of multiparent populations can accelerate the identification of loci contributing to trait variation, facilitate recombination, and uncover multiple alleles at a locus that contribute to a trait [ 86 ]. In some crops, trait introgression efforts could also benefit from so-called “speed breeding” approaches, where day-night cycles, temperatures, and timing of seed harvest are manipulated to dramatically reduce the time required to grow out each generation of breeding lines [ 87 ]. When combined with marker-assisted backcrossing or genomic prediction and selection, traits of interest from a traditional variety donor can thus be rapidly introgressed while minimizing the genome-wide contribution of the donor.

For many perennial crops, including trees and clonally propagated species, conservation of wild relatives and traditional varieties can be complicated by the limited potential for reproduction by seed or the need to preserve particular strains. Species such as apple, citrus, grape, and avocado are preserved in living nurseries, where many plants may consist of distinct genetics aboveground grafted to rootstocks that are tolerant of local growing conditions and soil pests [ 88 ]. While these collections may occupy large tracts of land with “permanent” plantings, they also offer some advantages. These include the opportunity to observe and harvest fruits grown under a variety of weather conditions over many growing seasons.

Safeguarding diversity for the future

We are losing genetic resources from farmers’ fields through the replacement of traditional varieties by modern cultivars [ 89 ] and from the wild in part due to the very same global challenges that their use could contribute to solving: climate and environmental changes [ 90 , 91 ]. Conserving and making available crop genetic resources for current and future use in breeding, research and cultivation is the core mandate of genebanks all around the world [ 92 ]. While global germplasm collections are far from complete, and germplasm samples of traditional varieties and natural populations of crop wild relatives are particularly under-collected and under-conserved [ 93 , 94 ], they remain a critical—and in some cases our only—source of wild relative variation. Triticum tiompheevii , for example, is a wild relative of wheat that is most likely extinct in situ but still available from ex situ collections [ 95 ].

Germplasm repositories are increasingly pushing to make their resources available. They do so most effectively by sharing germplasm under agreed terms as described under the multilateral system of the International Plant Treaty [ 96 ] or similar arrangements that are set up to encourage use by private and public sector users. Similarly, large-scale, systematic initiatives are taking place to evaluate and assess traits of interest in germplasm samples [ 82 , 84 , 97 , 98 ], providing key data that is useful for initiating pre-breeding programs. Taking advantage of these resources, including traditional variety germplasm and the diverse wild relative alleles they contain, may well prove key in adapting crops to rapidly changing global environments.

Acknowledgments

We would like to acknowledge Felix Andrews for helping us realize how not to think about domestication.

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Chapter 8: Inheritance of Quantitative Traits

Laura Merrick; Kendra Meade; Arden Campbell; Deborah Muenchrath; Shui-Zhang Fei; and William Beavis

Introduction

Many of the traits that plant breeders strive to improve are quantitatively inherited. For example, breeding efforts targeting quantitative traits have allowed major increases in crop yield during the past 80 years or so. A quantitatively inherited trait is controlled by many genes at different loci, with each gene — known as a polygene — contributing a small effect to the expression of the character. Polygenes are also known quantitative trait loci (QTL) . QTLs involved in expression of a quantitative character act cumulatively to determine the phenotype of the trait. Their mode of inheritance is called quantitative genetics. Quantitative genetics describes the connection between phenotype and genotype and provides tools to show how phenotypic selection of complex characters changes allele frequencies.

Quantitative genetics focuses on the nature of genetic differences, seeks to determine the relative importance of genetic vs. environmental factors, and examines how phenotypic variation relates to evolutionary change. Typically, quantitative genetic analysis is executed on traits showing a continuous range of values. Analysis of quantitative traits is based on statistical predictions of population response. Examples of quantitatively inherited traits include yield, vigor, rate of photosynthesis, protein content, and drought tolerance.

Key Concepts

Central to the mathematical modeling of quantitative genetics is the concept of recognition of family resemblance. If genes influence variation in a trait (and sources of environmental variation are minimized or controlled), related individuals would be expected to resemble one another more than unrelated ones. Siblings should resemble each other more than distantly related relatives. A comparison of plant individuals with different degrees of relatedness provides information about how much genes influence the character.

Several factors influence the likelihood of progress when breeding for quantitatively inherited traits, including:

• Interaction of the multiple genes contributing to the phenotype;
• Gene actions of the respective genes involved; and
• Frequencies of those genes.

Various models are used to distinguish genetic, environmental, and genetic x environmental interaction effects on phenotype and to improve breeding efficiency of quantitative traits.

• Distinguish environmental and hereditary variation and be aware of why detecting the interaction of genetic and environmental factors is important.
• Understand the attributes of quantitative inheritance.
• Be familiar with statistical methods applied to quantitative inheritance.
• Study the types of gene actions and interactions affecting quantitative traits.
• Examine the genetic advance from selection formula and be able to explain how each of its components influences the improvement of the selected characteristic.
• Learn the types of heritability estimates and their importance in plant breeding.

Heritable vs. Environmental Variation

The phenotype of a plant or group of plants is modeled as a function of its genotype as modified by the environment.

Phenotype = Genotype + Environment + (Genotype x Environment)

[latex]P = G + E + (G \times E)[/latex]

Some characters are more responsive or sensitive to growing conditions than others . Qualitative traits such as flower color are not strongly influenced by the environment. On the other hand, quantitative traits such as grain yield or abiotic stress tolerance are influenced markedly by the environment. The degree of sensitivity or the range of potential responses to the environment is determined by the genetic composition of the individual plant or population of plants.

Genetic variation is essential in order to make progress in cultivar improvement. However, sources of variation include:

• Environmental variation
• Genetic variation and
• Interaction of genetic and environmental variation

Plant breeders must distinguish among these sources of variation for the character of interest in order to effectively select and transmit the desired character or assemblage of characters to subsequent generations.

GxE Interaction Example

A classic example of genotype × environment interaction (GxE) involves studies conducted in the 1930s and 1940s by the ecologists Clausen, Keck, and Hiesey (1940, 1948). They collected plants from natural wild populations — principally yarrow (Achillea millefolia) and sticky cinquefoil (Potentilla glandulosa) — that grew along an east-west elevational transect in California, running from near sea level at the Pacific Ocean to more than 3000 m elevation in the Sierra Nevada mountains.

Both species exhibited a vast amount of variation in native populations with respect to growth form and other traits, including such attributes as plant height, winter survival, and number of stems produced. Through a series of reciprocal transplant experiments using cuttings or clonal material from wild populations, they tested the contribution of genetic and environmental variation to observed phenotypic variation among plants established in three main “common gardens” or transplant plots: Stanford, Mather, and Timberline (Fig. 1).

Conclusions

They concluded each species had differentiated into genetically distinct subspecies — which they called ecotypes — that are best suited to their specific environments. In the transplant gardens, no single ecotype performed best at all altitudes. For example, genotypes that produced the tallest plants at the mid-altitude garden site grew poorly at the low and high sites. Conversely, genotypes that grew the best at the low or high sites sometimes performed poorly at the mid-altitude site. Although within a species, all populations were found to be completely interfertile, ecotypes adapted to low or mid-altitude died when transplanted to the high altitude garden, while ecotypes from high elevations along the transect survived through the winter when locally grown in the test plots. GxE interaction was observed for height, among other characters.

Significance Illustration

Figure 3 shows variation in phenotype between two cultivars of watermelon with regard to a quantitatively inherited trait (yield in this case) in response to variation in an environmental factor (soil salinity in this case) and illustrates the significance of genotype x environmental interaction.

Characteristics of Quantitative Traits

Inheritance of quantitative traits involves two or more nonallelic genes (multiple genes or polygenes); the combined action of these genes, as influenced by the environment, produces the phenotype. The effect of individual genes on the trait is not apparent. However, early in the 1900s it was discovered that the inheritance of the individual genes contributing to the phenotype of quantitative traits do indeed follow the same Mendelian inheritance principles as simply-inherited genes.

Inheritance of Quantitative Traits

In 1909, Herman Nilsson-Ehle, a Swedish geneticist and wheat breeder, conducted some of the classic studies on quantitatively inherited traits in wheat. He developed what is known as the “Multiple Factor” or multi-factorial theory of genetic transmission. A key observation made by Nilsson-Ehle was that although a spectrum of continuous variation in kernel color (a quantitatively inherited trait influenced by environmental factors) could be observed in segregating generations, he was able to determine that segregation for these genes fit a model that each separate contributing gene followed a pattern of Mendelian inheritance .

Bread wheat is a hexaploid — allopolyploid that contains three slightly different, but similar ancestral genomes (referred to as A, B, and D) in its genome (AABBDD). Depending on the cultivars that Nilsson-Ehle studied, each genome had a single gene that affected kernel color, and each of these loci has a red allele ( R ) and a white allele ( r ). Alleles at each locus varied slightly in their effect on kernel color, and will be designated in this example by different superscripts, e.g., R 1 or r 3 .

He crossed two cultivars of wheat that varied in kernel color, one with dark red seeds (homozygous dominant genotype R 1 R 1 R 2 R 2 R 3 R 3 , based on the symbols designating the ancestral genomes) and another with white kernels (homozygous recessive r 1 r 1 r 2 r 2 r 3 r 3 ). He noted that the F 1 of a cross between these parents (heterozygote R 1 r 1 R 2 r 2 R 3 r 3 ) was intermediate in color (light red), but the F 2 generation could be grouped into seven classes, ranging in color from dark red to white. He explained the distribution on the basis of three pairs of genes segregating independently, with each dominant allele contributing to the intensity of the red color.

With three independent pairs of genes segregating, each with two alleles, as well as environmental effects acting on kernel color, the F 2 progeny would contain 63 plants with varying shades of red kernels and one with white kernels. Linkage among the genes restricts independent assortment, so that the required size of the F 2 population becomes larger.

Characteristics Indicative of Quantitative Inheritance

There are three general types of traits that are quantitatively inherited: continuous, meristic, and threshold. An example of the first type, a continuous trait, is fruit width of pineapple. The second type, a meristic character, is a countable trait that can take on integer values only, e.g., number of tillers of maize or branches of a rose bush. The third type of quantitative trait is known as a threshold character or “all-or-none” trait. Such traits are typically ranked simply as presence or absence, e.g., Downy Mildew disease in soybean.

Although they have only two phenotypes, threshold traits are considered to be quantitatively inherited because their expression depends on a liability (such as disease susceptibility or tolerance of nicotine levels) that varies continuously. Heritability of these traits is a function of the incidence of the trait in the population, so it is difficult to determine the importance of genetic factors in different environments or in different populations that differ in incidence. Threshold traits are assumed to be represented by an underlying normally distributed “liability trait” that is the sum of the independent genetic and environmental components of the distribution. A disease would have to be present before you could determine if certain genotypes were susceptible or not. For example, plants might be able to tolerate low to moderate levels of nicotine in their tissues until a threshold was crossed, above which the high level of nicotine present would be lethal.

Threshold characters exhibit only two phenotypes — the trait is either present or absent — but the susceptibility to the trait varies continuously and environmental components of the distribution. A disease would have to be present before you could determine if certain genotypes were susceptible or not. For example, plants might be able to tolerate low to moderate levels of nicotine in their tissues until a threshold was crossed, above which the high level of nicotine present would be lethal.

Threshold characters exhibit only two phenotypes — the trait is either present or absent — but the susceptibility to the trait varies continuously.

Environment has a large influence on the trait’s phenotype. That is, for the particular trait, the relative responses of plants change when grown under different environmental conditions.

Distinct segregation ratios of individual nonallelic genes are not observed. Recombination and segregation patterns are based on the combined effect of the polygenes on the trait. The more loci controlling the character, the greater the complexity.

Genes may differ in their individual gene action, but their effect on the trait is cumulative. Types of gene action include additive, dominance, overdominance and epistasis. Effects of gene action using the concepts of genotypic and breeding values are discussed in Appendix A.

The genotypic value is equal to

[latex]G = A + D + I[/latex]

• G = genotypic value of all loci considered together
• A = sum of all additive effects (i.e. breeding values) for separate loci
• D = sum of all dominance deviations (i.e., interaction between alleles at a locus or so-called intralocus interactions)
• I = interaction of alleles among loci (also referred to as the deviation or epistatic deviation)

For an individual, the breeding value is calculated by the summation of the average effects of its genes (also referred to as the additive effect of genes). The average effect of an allele is approximately the average deviation of the mean phenotypic value from the population mean if the allele at a particular locus is substituted by another allele (Falconer and Mackay 1996).

Transgressive segregation may occur. These individuals exhibit phenotypes outside the range of those expressed by the parents. Transgressive segregation occurs when progeny contain new combinations of multiple genes with more positive effects or more negative effects for the quantitative trait than found in either parent. One challenge is that strong environmental effects would make it difficult to assess the mean performance in parental plants vs. progeny in order to detect for the presence of any transgressive segregants.

Measurement of Continuous Variation

Analysis of inheritance of qualitative traits is generally concerned with individual matings and their progeny and is made by counts and ratios. In contrast, analysis of quantitative traits is concerned with populations of organisms that consist of many possible kinds of matings; analysis of such traits is made by use of statistics. Statistical methods provide a tool for describing and evaluating quantitatively inherited characters. Since it is impractical to examine an entire population, plant breeders sample the population(s) of interest. The sample must be representative of the population — the sample must be:

• large enough to include the entire range of variability of the trait that occurs in the population, and
• random to avoid introducing any bias.

Thus, the greater the variability within the population, the larger the sample size that is required to accurately describe that population. (Throughout this and subsequent modules, you can generally assume that we’re referring to a representative sample, rather than a population.)

Statistics may be descriptive or analytical. [ See Appendix B to briefly review some statistical terms and concepts ].

The study of quantitative traits is sometimes referred to as “statistical genetics” because of its reliance on statistical methods. In order to understand the inheritance of quantitative characters and the methods applied to these characters, it is essential that you become familiar with fundamental statistics. A basic review is provided here.

When using symbols to represent these population parameters, it is important to distinguish between information about the population and that concerning a sample representing the population.

Similar Statistical Symbols

As a shorthand, statistics commonly use symbols to convey concepts. Often there are several symbols that relate to very similar, but slightly differing concepts. Here’s a list of symbols related to means, variance, and standard deviation that you will encounter in this lesson. The latter two parameters describe the variability or dispersion about the mean of the population or sample derived from a population.

Although the differences between these are important from a statistical perspective, they are commonly used synonymously.

Descriptive Statistics

Range — the lowest and highest phenotypic values in the population or sample for the character.

Mean ( µ or M for population; [latex]\bar{X}[/latex] for sample) — describes the average performance of a random sample from a population for a trait. The mean is a measure of central tendency — it does not tell anything about the distribution of individual observations. Mean equals the sum of the trait values of each individual divided by the number of samples (n):

[latex]\bar{x} = \frac{\Sigma x}{n}[/latex]

Variance ( V or σ 2 for population; s 2 for sample) — a measure of the scatter or dispersion of phenotypic values. The greater the variability among individuals, the greater the variance. Two populations with the same mean for the same character could differ greatly in their respective variance for that character.

Average the squared deviations from the mean, (X – X) 2 :

[latex]V = \sigma^{2} = \frac{\Sigma [(x - \bar{x})^{2}]}{n - 1}[/latex]

Standard deviation ( σ for population; s for sample) — also a measure of dispersion around the mean. Standard deviation expresses the dispersion in the same unit as the mean.

Standard deviation is the square root of the variance.

[latex]\sigma = \sqrt{V} \text{ or } \sqrt{\sigma^{2}}[/latex]

A small variance and a small standard deviation tell us that the phenotypic values are near the mean value. In contrast, a large variance and standard deviation indicate that the trait values have a wide range.

Samples in which the observations are clustered closely around the mean (red) have a smaller variance and standard deviation than observations dispersed widely (blue).

Coefficient of variation (CV) — the standard deviation as a percentage of the mean. Because the units cancel out, CV is a unitless measure.

Divide the standard deviation by the mean and multiply by 100:

[latex]CV = \frac{\sigma}{\bar{X}} \times 100[/latex]

A CV of about 10% or less is desirable in assessing biological systems. When the CV is greater than 10%, the variability in the sample or the population may be too great to sort out the factors contributing to that variability.

The mean and the variance are used to describe an individual characteristic, but plant breeders are frequently interested in more than one trait simultaneously . Two or more characteristics can vary together, and are thus not independent of one another.

Covariance provides measure of the strength of the correlation or dependent relationship between two or more sets of variables. When two traits are correlated, a change in one trait is likely to be associated with a change in the other trait. Variance is a special case of covariance that is the covariance of a variable with itself. Correlations between characteristics are measured by a correlation coefficient ( r ).

Covariance — the mean value of the product of the deviations of two random variables from their respective means. For example, the covariance of two random variables, x and y is expressed as:

[latex]Cov_{x,y} = \frac{\Sigma (x_{i} - \bar{x})(y_{i} - \bar{y})}{n - 1}[/latex]

Correlation coefficient (r) — measures the interdependence of two or more variables and is obtained by dividing the covariance of x and y by the product of the standard deviations of x and y. The correlation coefficient can range from -1 to +1.

[latex]r = \frac{Cov_{x,y}}{S_{x}S_{y}}[/latex]

Correlation is a “scaled” version of covariance and the two parameters always have the same sign—positive, negative, or zero (0). When the sign is positive, the variables are positively correlated; when it’s negative, they are said to be negatively correlated, and when it’s zero, the variable are described as being uncorrelated. But it is important to note that a correlation between variables indicates that they are associated, but it does not imply a cause-and-effect relationship.

QTLs and Mapping

The relative importance of genetic and environmental factors for a given trait can be estimated by the phenotypic resemblance between relatives. Until recently, quantitative genetics focused on phenotypic information, but increasingly molecular biology tools are being applied in an effort to locate where quantitative trait loci (QTLs) occur in plant and animal genomes. Various genetic markers have been identified and mapped, allowing identification of QTLs by linkage analysis .

A common method for mapping QTLs is to cross two homozygous lines that have different alleles at many loci. The F 1 progeny are then backcrossed and intercrossed to allow genes to recombine through independent assortment and crossing over. Offspring in segregating generations are examined for correlations between inheritance of marker alleles and phenotypes that are quantitatively inherited. QTL mapping will be covered in more detail in later courses on Molecular Genetics and Biotechnology and Molecular Plant Breeding .

Multiple Genes and Gene Action

The general types of gene action for quantitative characters (additive, full and partial dominance, and over-dominance) do not differ from those for qualitative traits. However, the genes contributing to the phenotype of a quantitative character may or may not differ in their individual gene action, and their relative effects on the trait’s expression may differ. Some may have major influence and others may have only minor effects on the phenotype. The genes controlling a quantitative trait may also interact. The table below gives examples of types of gene action at two loci. For quantitative traits this would be expanded to multiple loci.

In the preceding examples, A and B are assumed to have equal effects. However, this often may not be true because genes at different loci may affect the expression of the trait in different ways. Some QTLs may be genes with major effect, while others may contribute only a minor effect. Penetrance or expressivity (refer to Deviations from Expected Phenotypes in the “Gene Segregation and Genetic Recombination” module) may influence trait expression. Likewise, pleiotropic effects (refer to Gene Interactions in the “Gene Segregation and Genetic Recombination” module) may be present, affecting different traits in different ways.

Heritability

Conceptual basis for understanding heritability.

Heritability estimates the relative contribution of genetic factors to the phenotypic variability observed in a population. What causes variance among plants and among lines or varieties? Phenotypic variation observed among plants or varieties is due to differences in

• their genetic makeup,
• environmental influences on each plant or genotype, and
• interaction of the genotype and environment.

The effectiveness with which selection can be expected to take advantage of variability depends on how much of that variability results from genetic differences. Why? Only genetic effects can be transmitted to progeny. Heritability estimates

• the degree of similarity between parent and progeny for a particular trait, and
• the effectiveness with which selection can be expected to take advantage of genetic variability.

Family Resemblance

As mentioned in the introduction of this lesson, central to the understanding of quantitatively inherited traits is the recognition of family resemblance. Two relatives, such as a parent and its offspring, two full or half-siblings, or identical twins, would be expected to be phenotypically more similar to each other than either is to a random individual from a population. Although close relatives may share not only genes (they may also share similar environments for traits that have a large genetic component), resemblance between relatives is expected to increase as closer pairs of relatives are examined because they share more and more genes in common. In this conceptual framework, heritability can be understood as a measure of the extent to which genetic differences in individuals contribute to differences in observed traits.

Statistical Basis for Understanding Heritability

For plant breeders, heritability can also be understood in a statistical framework by defining it as the proportion of the phenotypic variance that is explained by genetic variance. Heritability indicates the proportion of the total phenotypic variance attributable to genetic effects, the portion of the variance that is transmittable to offspring. A general formula for calculating heritability is

[latex]\text{Heritability} = \frac{V_{G}}{V_{P}}= \frac{V_{G}}{V_{G} + V_{E} + V_{GE}}[/latex]

[latex]=\frac{\sigma^{2}_g}{\sigma^{2}_{ph}} = \frac{\sigma^{2}_g}{\sigma^{2}_g + \sigma^{2}_c + \sigma^{2}_{ge}}[/latex]

[latex]V_{P} = V_{G} + V_{E} + V_{GE}[/latex]

[latex]\sigma^{2}_{ph} = \sigma^{2}_{g} + \sigma^{2}_{e} + \sigma^{2}_{ge}[/latex]

[latex]V_{P} = \sigma^{2}_{ph}[/latex] = phenotypic variance (total variance of the population)

[latex]V_{G} = \sigma^{2}_{g}[/latex] = genotypic variance (variance due to genetic factors)

[latex]V_{E} = \sigma^{2}_{e}[/latex] = environmental variance (variance due to environmental factors)

[latex]V_{GE} = \sigma^{2}_{ge}[/latex] = genotype x environmental variance (variance due to interactions between genotypes and environmental factors)

Uses of Heritability Estimates

Heritability serves as a guide for making breeding decisions. It is generally used to

• determine the relative importance of genetic effects which could be transferred from parent to offspring
• determine which selection method would be most likely to improve the character
• predict genetic advance from selection

A key point to understand is that heritability is a population concept — application of heritability estimates is restricted to the population on which the estimate was based and to the environment in which the population was grown. However, some characters exhibit fairly consistent estimates (either high or low) among populations (within species) and environments. When considering characters that have high heritability, what we expect to observe for each genotype is that its phenotype will be quite predictable over a range of environments (growing conditions). In other words, for characters with high heritability, genotype fairly accurately predicts phenotype. This is not so for characters with low heritability.

Heritability depends on the range of typical environments experienced by the population under study (if the environment is fairly uniform, then heritability can be high, but if the range of environmental differences is high, then heritability may be low. Even when heritability is high, environmental factors may influence a characteristic. Heritability does not indicate anything about the degree to which genes determine a trait; instead it indicates the degree to which genes determine variation in a trait.

Characters having low heritabilities are usually highly sensitive to the environment, presenting greater breeding challenges — low heritability traits often require larger populations and more test environments than do characters having high heritabilities for selection and improvement.

Broad-Sense Heritability

Types of heritability.

There are two types of heritability: broad-sense and narrow-sense heritability.

Broad sense heritability, H 2 , estimates heritability on the basis of all genetic effects.

[latex]H^{2} = \frac{V_{G}}{V_{P}} \times 100[/latex]

[latex]= \frac{\sigma^{2}g}{\sigma^{2}_{ph}} \times 100[/latex]

It expresses total genetic variance as a percentage, and does not separate the components of genetic variance such as additive, dominance, and epistatic effects.

Generally, broad-sense heritability is a relatively poor predictor of potential genetic gain or breeding progress. Its usefulness depends on the particular population. Broad-sense heritability is

• more commonly used with asexually propagated crops than with sexually propagated agronomic crops
• applied to early generations of self-pollinated crops

Narrow-Sense Heritability

Narrow-sense heritability, h 2 , in contrast, expresses the percentage of genetic variance that is caused by additive gene action, V A .

[latex]h^{2} = \frac{V_{A}}{V_{P}} \times 100[/latex]

[latex]= \frac{\sigma^{2}_{A}}{\sigma^{2}_{ph}} \times 100[/latex]

Narrow-sense heritability is always less than or equal to broad-sense heritability because narrow-sense heritability includes only additive effects, whereas broad-sense heritability is based on all genetic effects.

The usefulness of broad- vs. narrow-sense heritability depends on the generation and reproductive system of the particular population. In general, narrow-sense heritability is more useful than broad-sense heritability since only additive gene action can normally be transmitted to progeny. This is, because in systems with sexual reproduction, only gametes (alleles) but not genotypes are transmitted to offspring. In contrast, in case of asexual reproduction, genotypes are transmitted to offspring.

Estimating Heritability

As the formulas presented above indicate, heritability is calculated from estimates of the components of phenotypic variation: genetic, environmental, and genetic x environmental interactions. Two main approaches are described here to help estimate the contribution of different G, E, and GxE components and for calculating heritability. One approach focuses on eliminating one or more variance component, while the other focuses on comparing the resemblance of parents and offspring. These estimates can be determined from an analysis of variance or regression analysis of the character performance of a population grown in several environments (multiple locations and/or years).

Testing a character’s performance in multiple environments (e.g., more than one location and/or years) is essential to get an accurate estimate of the environmental effects on the character. Test environments should be either random or representative of the target environment the type of environment for which the cultivar under development is intended. Heritability is based on variance, the average of the squared deviations from the mean—a statistical measure of how values vary from the mean. Testing in a single environment provides no measure of variance. The greater the number of environments used in the character’s evaluation, the better is the reliability of the variance estimate, as well as the heritability estimate. Without adequate testing in multiple environments, heritability estimates may be misleading.

When evaluating different genotypes for a specific character, if the genotypes vary widely in response to differing environments, environmental variance will be relatively high and heritability for the character low. Conversely, if the different genotypes perform in a similar manner across environments, e.g., certain genotypes are always among the best and others always the poorest regardless of environment, environmental variance will be low and heritability high.

Estimation Using Analysis of Variance (ANOVA)

Estimating heritability from an analysis of variance provides a way to measure the relative contributions of two or more sources of variability.

Several analytical procedures are commonly used to sort out the sources of variation in the sample, to determine the relationship among factors contributing to the variability, and to estimate the heritability of the character.

Analysis of Variance — this procedure identifies the relative contribution of the sources of observed variation in the sample. Sources of variation may include environment, replication, or genotypic effects. The portion of variation that cannot be attributed to known causes is called “error.”

In this example, phenotypic variance is explained by differences in genetic composition, as well as to unknown factors. r stands for the number of replications.

[latex]\sigma^{2}_{ph} = \sigma^{2} + r \sigma^{2}_{g}[/latex]

The particular steps involved in this procedure and the analysis of variance table that results depend on the design of the experiment. Analysis of variance procedures and interpretation are discussed in the Quantitative Methods course.

Estimation using Parent-Offspring Regression

Heritability can also be estimated by evaluating the similarities between progeny and parent performance using regression analysis . This analysis is based on several assumptions.

• The particular character has diploid, Mendelian inheritance.
• There is no linkage among loci controlling the character of interest, or the population is in linkage equilibrium .
• The population is random mated.
• Parents are not inbred.
• There is no environmental correlation between the performance of parents and progeny (to avoid violating this last assumption, randomize parents and progeny within replications; i.e., do not test them in the same plot).

The linear regression model is:

[latex]Y_{i} = a + bX_{i} + e_{i}[/latex]

• [latex]Y_{i}[/latex]= phenotypic value of progeny of the i th parent
• a = mean phenotypic value of all parents tested
• b = regression coefficient (slope of the line)
• [latex]x_{i}[/latex]= phenotypic value of the i th parent
• [latex]e_{i}[/latex]= experimental error in the measurement of X i

Regression — this procedure examines the strength of the relationship between factors or the influence one factor has on another. The linear regression procedure fits a straight line to a scatterplot of data points. The general equation for the regression line is:

[latex]y = a + bx[/latex]

• y = response or dependent variable
• x = predictor or independent variable
• a = y-intercept of the line
• b = regression coefficient, the slope of the line

The regression line always passes through the point ([latex]\bar{x}, \bar{y}[/latex]). Relative to the total spread of the data, if most of the datapoints lie on or very near the line, there is a strong relationship between the predictor and response variables—x has a strong influence on y. In contrast, the fewer the points that fall on or near the line, the less influence x has on y. A cautionary note: although the x variable may have strong influence on the y variable, x may not be the cause of the y response, nor the sole factor influencing y.

Regression can be used to assess the relative effect of environment on phenotypic value or to obtain information about gene action. The relative scatter about the regression line of a plot of genotype (the predictor variable, x) against phenotypic value (the response variable, y), provides information about gene action. For example, regression analysis of the following two examples suggests that the gene action in example 1 (left panel) is additive (no dominance), whereas there is a complete dominance (by the A 2 allele) in the case of example 2 (right panel) (Fehr, 1987).

When the heterozygous genotype has a value midway between the two homozygotes and thus all three genotypic values fall on the linear regression line the only gene action contributing to the phenotype is additive.

Regression analysis of phenotypic values of progeny (y) against parents (x) provides useful information about the degree of similarity of progeny to the parents.

Alternative Formula

An alternative formula for calculating the regression coefficient, b, is

[latex]b = \frac{\Sigma(X - \bar{X})(Y - \bar{Y})}{\Sigma (X - \bar{X})^{2}}[/latex]

• b = regression coefficient
• X = parent values
• Y = progeny values

The performance of the progeny is a function of the genetic factors inherited from the parents. (Assume that “parent” means either a random plant or line from a population.) Thus, X, the parent value, is the independent variable, and Y, the progeny value, is the response or dependent variable.

Regression Coefficient

What does the regression coefficient, b, tell us?

If b = 1, then

• gene action is completely additive,
• negligible environmental effect,
• and negligible experimental error.

The smaller the value of b, the less closely the progeny resemble their parent(s), indicating

• greater environmental influence on the character,
• greater dominance and/or epistatic effects, and/or
• greater experimental error.

An analysis of variance will provide estimates of the relative influence of genetics, environment, and experimental error.

The type of heritability and the specific formula used to estimate it depends on the type of progeny evaluated.

Types of Progeny

The population’s reproductive mode and mating design determine the type of heritability and the formula used to calculate the estimate.

Selfed progeny

• F 2 plants are self-pollinated to obtain the F 3 . All the alleles in the F 3 come from the F 2 parent. Evaluate the character performance of the F 3
• Regress the performance of the F 3 on the performance of their F 2 parents.
• The regression coefficient, b, is equal to H 2 , broad-sense heritability because its genetic variance includes dominance, additive, and epistatic effects. [latex]H^{2} = b \times 100[/latex]
• Since it is difficult to obtain information about gene action (dominance, epistasis, additive) in self-pollinated populations, narrow-sense heritability is a poor predictor of genetic gain and rarely used in these populations. Inbreeding causes an upward bias in the heritability estimate.

Full-sib progeny

• Two random F 2 plants are mated. Half of the alleles in the F3 come from one parent and half from the other. Evaluate the character performance of the F3.
• Determine the mid-parent value of the two parents. Mid-parent value, X = (x 1 + x 2 ) /2
• Regress the progeny on the mid-parent value. The regression of progeny on the mid-parent value is [latex]\frac{ \frac{1}{2} V_A}{\frac{1}{2} V_P} = \frac{ \frac{1}{2} \sigma^2 _A}{\frac{1}{2} \sigma_{ph}} = \frac{\sigma^2_A}{\sigma^2_{ph}}[/latex]

Since the progeny have both parents in common, only additive variance is included, so the regression coefficient, b, is equal to narrow-sense heritability.

[latex]h^{2} = b \times 100[/latex]

Half-sib progeny

• Open-pollinate F 2 plants. Seed will be harvested from each F 2 individual separately. Progenies from the same F 2 plant have the maternal parent (the respective F 2 plant) in common, while the paternal parent is pollen from the whole F 2 population. Thus all offspring from an F 2 plant after open pollination are “half-sibs”.
• Regress the performance of the half-sib progenies on their parents. [latex]b = \frac{\sigma^{xy}}{\sigma^{2}_{x}}[/latex] where: [latex]\sigma^{xy}[/latex] = covariance between parents, x, and their progeny, y [latex]\sigma^{2}_{x}[/latex] = phenotypic variation among parents
• The covariance between parents and progeny includes additive variance and some forms of additive epistasis (usually negligible), but no dominance variance. Thus, narrow-sense heritability can be estimated. The regression coefficient, b, is equal to half the heritability value.
• Multiply the regression coefficient by 2 to obtain narrow-sense heritability.

[latex]h^{2} = 2b \times 100[/latex]

Heritability Influences

Heritability is not an intrinsic property of a trait or a population. As we’ve seen, it is influenced by:

• population — generation, reproductive system, and mating design
• environment — locations and/or years
• experimental design — experimental unit (plant, plot, etc.), replication, cultural practices, techniques for data gathering.

Heritability can be manipulated by increasing the number of replicates and number of environments sampled (in space and/or time). Genetic variance can be increased by using diverse parents and by increasing the selection intensity. Heritability is only an indicator to guide the breeder in making selections and is not a substitute for other considerations, such as breeding objectives and resource availability.

Genetic Advance from Selection

Estimating response to selection.

Evolution can be defined as genetic change in one or more inherited trait that takes place over time within a population or group of organisms. Plant breeders can use quantitative genetics to predict the rate and magnitude of genetic change. The amount and type of genetic variation affects how fast evolution can occur if selection is imposed on a phenotype.

The amount that a phenotype changes in one generation is called the selection response, R . The selection response is dependent on two factors—the narrow-sense heritability and the selection differential, S . The selection differential is a measure of the average superiority of individuals selected to be parents of the next generation.

[latex]R = h^{2}S[/latex]

The above equation is often called the “breeder’s equation”. It shows the key point that response to selection increases when either the heritability of the trait or the strength of the selection increases.

Realized Heritability

In an experiment, the observed response to selection allows the calculation of an estimate of the narrow-sense heritability, often called the realized heritability. A low h 2 (<0.01) occurs when offspring of the selected parents differ very little from the original population, even though there may be a large difference between the population as a whole and the selected parents. Conversely, a high h 2 (> 0.6) occurs when progeny of the selected parents differ from the original population almost as much as the selected parents.

In the figure in the next screen, it can be seen that the selection differential (S) in each generation is the difference between the mean of the entire original population and the mean of group of individuals selected to form the next generation. In contrast, the response to selection (R) indicates the differences in population means across generations. The value of R is the difference between the mean of the offspring from the selected parents and the mean of the entire original population:

[latex]S = \bar{T}_{S} - \bar{T}[/latex]

[latex]R = \bar{T}_{O} - \bar{T}[/latex]

• T = mean of the entire original population
• T S = mean of selected parents
• T O = mean of the offspring of the selected parents

Adaptive Value

The proportionate contribution of offspring of an individual to the next generation is referred to as fitness of the individual. Fitness is also sometimes called the adaptive value or selective value. Note in the figure that the non-selected members of the population do not contribute to the next generation and that selection over time reduced the variance of the population.

Types of Selection

Artificial selection refers to selective breeding of plants and animals by humans to produce populations with more desirable traits. Artificial selection is typically directional selection because it is applied to individuals at one extreme of the range of variation for the phenotype selected. This type of selection process is also called truncation selection because there is a threshold phenotypic value above which the individuals contribute and below which they do not. In contrast, under natural selection in non-managed populations, other types of selection may occur.

Three main types of selection are generally recognized. All three operate under natural selection in natural populations, whereas under artificial selection via selective breeding by humans only directional selection is common.

Directional Selection

Directional selection acts on one extreme of the range of variation for a particular characteristic.

Stabilizing selection

Stabilizing selection works against the extremes in the distribution of the phenotype in the population. An example of this type of selection is human birth weight. Infants of intermediate weight have a much higher survival rate than infants who are either too large or too small.

Disruptive selection

Disruptive selection favors the extremes and disfavors the middle of the range of the phenotype in the population.

One of the most famous longest-term selection experiments is a study conducted by University of Illinois geneticists who have been selecting maize continuously for over 100 generations since 1896. They have been changing oil and protein content in separate experiments, selecting for either high or low content. In some cases after multiple generations, they have shifted selection from high to low or vice versa.

Expected Gain From Selection

Because resources are limited, the breeder’s objective is to carry forward as few plants or lines as possible without omitting desirable ones. How does the breeder decide how many and which plants or lines within a population to carry forward to the next generation? The breeder can use heritability estimates to predict the probability that selecting a given percentage of the population or selection intensity, i , will result in progress. The expected progress or gain can be calculated using this formula:

[latex]G_C = (k) (\sqrt{V_P})(h^2) = k \sigma_{ph} h^2[/latex]

• G c = expected gain or predicted genetic advance from selection per cycle
• k = selection intensity — a constant based on the percent selected and obtained from statistical tables (note that some people use hte i symbol instead of k for selection intensity
• [latex]\sqrt{V_{p}}[/latex] or [latex]\sigma_{ph}[/latex] = square root of phenotypic variance (equivalent to standard deviation)
• h 2 = narrow – sense heritability in decimal form (narrow – sense is used for sexually reproduced populations whenever possible, and broad sense heritability, H 2 , is used for self – pollinating and asexually reproduce populations)

Caution : The phenotypic values must exhibit a normal, or bell-curve, distribution for G c to be valid

As long as the distribution of phenotypic values is normally distributed, selection intensity values (symbolized by k or sometimes i) can be found in statistical tables. The intensity of selection practiced by plant or animal breeders depends just on the proportion of the population in the selected group.

The selection intensity is a standardized selection differential and is a measure of the superiority of the individuals selected as parents for breeding relative to the population from which they were selected. Representative values of k are shown in the table.

For Your Information

As will be explained in the next section, a key statistic used to describe populations is the mean performance of the population of genotypes. The mean performance of a population can be described by a combination of values for performance of both homozygous and heterozygous genotypes, as well as the relative frequency of alleles.

For illustration using a locus with two alleles, A 1 and A 2 , the genotypic value of the homozygotes is designated as A 1 A 1 =+a and A 2 A 2 = -a, while the heterozygous genotype is A 1 A 2 = d.

The value of a is the performance of a homozygous genotype minus the average performance of the two homozygous genotypes.

[latex]+a = A_1A_1 - \frac{(A_1A_1 + A_2A_2)} {2} [/latex]

[latex]-a = A_2A_2 - \frac{(A_1A_1 = A_2A_2)}{2}[/latex]

The value of d measures the degree of dominance between alleles, and is the difference between the value of the heterozygote and the mean of the homozygotes.

[latex]d = A_1A_2 - \frac{(A_1A_1 + A_2 A_2)}{2}[/latex]

Examples of relative genotypic values are given here in depictions showing a and d under different types of gene action.

The difference between the depictions of partial dominance show an example of how the effect of an allele can vary depending on whether the locus is a major gene or minor. Remember, additivity of genetic effects for quantitative traits does not mean that there are equal effects of all alleles at a locus or all loci affecting the trait.

Populations can be characterized by the amount and type of genetic variability contained within them. Genetic improvement of a quantitative character is based on effective selection among individuals that differ in what is known as the genotypic value. Variation among the genotypic values represents the genotypic variance of a population.

The genotypic value is the phenotype exhibited by a given genotype averaged across environments. A related concept is the breeding value, which is the portion of the genotypic value that determines the performance of the offspring. Genotypic value is property of the genotype and therefore is a concept that describes the value of genes to the individual , whereas breeding value describes the value of genes to progeny and therefore helps us understand how a trait is inherited and transmitted from parents to offspring . Remember that only additive genetic effects can be passed on to progeny. Non-additive genetic effects and environmental effects cannot be inherited by offspring.

Barbour, M.G, J.H. Burk, F.S. Gilliam, W.D. Pitts and M.W. Schwartz. 1999. Terrestrial Plant Ecology. 3rd edition. Benjamin Cummings, San Francisco, CA.

Clausen, J., D.D. Keck, and W. Hiesey. 1940. Experimental studies on the nature of species. I. Effects of varied environments on western North American plants. Carnegie Inst. Wash. Publ. 520.

Clausen, J., D. D. Keck, and W. M. Hiesey. 1948. Experimental studies on the nature of species. III. environmental responses of climactic races of Achillea. Carnegie Inst. Wash. Publi. 581.

Conner, J.K. and D.L. Hartl. 2004. A Primer of Ecological Genetics. Sinauer Associates, Sunderland, MA.

Falconer, D.S., and T.F.C. Mackay. 1996. Introduction to Quantitative Genetics. 4th edition. Longman Publ. Group, San Francisco, CA.

Hallauer, A. R. and J. B. Miranda. 1988. Quantitative Genetics in Maize Breeding. 2nd Edition. Iowa State University Press, Ames, IA.

Hill, W.G. 2005. A century of corn selection. Science 307: 683-684.

Pierce, B. A. 2008. Genetics: A Conceptual Approach. 3rd edition. W.H. Freeman, New York.

Rausher, M.D.. 2005. Example of Clausen, Keck, and Hiesey Experiment, Lecture 1-Lineages, Populations, and Genetic Variation. Online lecture notes from course on Principles of Evolution.

Department of Crop Science, University of Illinois at Urbana-Champaign. Values obtained for protein in the strains selected for oil and the values for oil obtained for the strains selected for protein each generation (1896-2004). 2007.

(1) Genetic potential of an organism. (2) Genetic makeup of an individual.

(1) A genotype that contains one dominant and one recessive gene or two different co-dominant genes (Aa,bB,CD). (2) An individual that has two copies of the same allele at a locus, e.g. , AA or aaa or AAAA.

A line resulting from pre-breeding.

Mutations are the ultimate source of all genetic variation. Mutations can occur at all levels of genetic organization, classified mainly as either chromosome mutations or genome mutations . Chromosome mutations are discussed in this module. Chromosome alterations involve either single nucleotides or fragments of chromosomes and are either small-scale (one or a few nucleotides substituted, inserted, or deleted) or large-scale (deletions, insertions, inversions, or translocations involving large segments of chromosomes or duplications of entire genes). Genome mutations—involving changes in number of whole chromosomes or sets of chromosomes—will be covered separately in the module on Ploidy—Polyploidy, Aneuploidy, Haploidy.

Genetic variation —dissimilarity between individuals attributable to differences in genotype—that is generated by mutations is acted upon by various evolutionary forces. Evolutionary processes that alter species and populations include selection, gene flow (migration), and genetic drift—whether or not plants are cultivated or wild. Evolution can be defined as a change in gene frequency over time. The way that plants evolve is dependent on both genetic characteristics and the environment they face.

Genetic variation results from differences in DNA sequences and, within a population, occurs when there is more than one allele present at a given locus. Major processes that affect heritable variation in crop plants are topics emphasized throughout the lessons of this course. Changes in gene frequencies within populations caused by natural selection can lead to enhanced adaptation, while changes caused by human-directed selection can facilitate the development of useful genetic variability and selection of superior genotypes. Selection is the differential reproduction of the products of recombination—both within and between chromosomes.

Genetic Resources

Historically plant breeders seeking sources of variability were constrained in choice of parental materials or plant genetic resources that were interfertile within closely related gene pools. But a range of new techniques such as mutagenesis, genetic engineering ( transgenic or transformed plants), and in vitro methods (tissue culture, doubled haploids, induced polyploids) expand the source and scope of variability that can be used in crop improvement.

Our expanding understanding of the molecular basis of genetics has provided insights and technologies that further not only our basic understanding of genes and their regulation, but also provide additional tools for crop improvement. Molecular techniques enable breeders to generate genetic variability, transfer genes between unrelated species, move synthetic genes into crops, and make selections at the molecular, cellular, or tissue levels. Combining these laboratory techniques with conventional field approaches can shorten the time required to develop new or improved cultivars. The importance and application of molecular technologies are rapidly increasing.

These topics mentioned above—mutations, gene expression, genetic markers, sources of genetic variation, genetic engineering, and molecular breeding methods—will be briefly mentioned in this module, but covered in greater detail in the later courses including Plant Breeding Methods, Molecular Genetics, and Biotechnology and Molecular Plant Breeding.

• Recognize how mutations are classified and inherited, as well as how mutations affect structure, processes, and products of genes and chromosomes.
• Understand the basic principles of transcription and translation.
• Become familiar with sources of genetic variation for cultivated plants, including crop gene pools and genetic engineering methods.

Mutations as Heritable Change

Without heritable variation, any trait favored by selection will not be passed on to offspring. Mutation is defined as heritable change in genetic information. Mutations entail modification of the nucleotide sequence of DNA and consist of any permanent alteration of a DNA molecule that can be passed on to offspring. DNA is a highly stable molecule and it replicates with a high degree of accuracy. However changes in DNA structure and replication errors can occur. Mutation involves modifications in the sequence of bases in DNA transmitted through mitosis and meiosis.

A nucleotide consists of a sugar molecule ( ribose in RNA or deoxyribose in DNA) attached to phosphate group and a nitrogen-containing base. In DNA or RNA molecules, each strand has a backbone of sugar and phosphate groups (Fig. 17).

CHEMICAL BASES IN DNA AND RNA

Two of the four nitrogenous bases in DNA— adenine (Fig. 13) and guanine (Fig. 14) are known as purines and the other two— cytosine (Fig. 15) and thymine (Fig. 16) are pyrimidines . Adenine, guanine, and cytosine are also found in RNA. Another pyrimidine known as uracil (Fig. 17) is the base used in RNA in place of thymine.

Some mutations occur in loci that encode for gene products such as proteins , and thus they may affect the processes of transcription , translation , or gene expression —processes that happen during the creation of proteins from the genetic code in DNA. But mutations also can occur in parts of the genome that do not code for any gene products (called noncoding DNA) or sequences that serve to control regulatory functions in the cell or chromosomes. For most loci, mutation changes allelic frequencies at a very slow rate and therefore consequences are negligible. Mutations may or may not change the phenotype of an organism. The majority of mutations that do occur are neutral in their effect and therefore do not have an influence on fitness. Some mutations are beneficial. But mutations can have deleterious effects, causing disorders or death.

Amino acids are a set of 20 different molecules used to build proteins. A peptide is one or more amino acids linked by chemical bonds (termed peptide bonds). Linked amino acids form chains of polypeptides (Fig. 18). The amino acid sequences of proteins are encoded in genes.

One or more polypeptides form the building blocks of proteins (Fig. 19). Proteins perform a variety of roles in cells.

Primary protein structure is a sequence of a chain of amino acids.

Secondary protein structure occurs when the sequence of amino acids is linked by hydrogen bonds.

Tertiary protein structure occurs when certain attractions are present between alpha helices and pleated sheets.

Quaternary protein structure is a protein consisting of more than one amino acid chain

Types of Mutations

Classification of mutations.

Mutations can occur at all levels of genetic organization, ranging from simple base nucleotide pair alterations to shifts and rearrangements in sequences of nucleotides along fragments of chromosomes to changes in the number and structure of whole chromosomes.

A mutation is a change from one hereditary state to another, e.g., allele A mutates to allele a . For a given locus, the normal allele is referred to as the ‘wildtype’ . Mutations are usually recessive and therefore their effects are hidden in heterozygotes. There are a number of common ways to classify mutations, including the following:

• causal agent
• rate or frequency of occurrence
• kind of tissue involved and its type of inheritance
• impact on fitness or function, or
• molecular structure and scale of the mutation.

Spontaneous vs. Induced Mutations

Depending on the cause, mutations can be either spontaneous or induced:

• Spontaneous mutations occur naturally with no intentional exposure to a mutagen . Spontaneous mutations can result from copying errors made during cell division.
• Induced mutations are caused by mutagens, either chemicals or radiation.

RARE VS. RECURRENT MUTATIONS

Recall from the module on Population Genetics, mutational events in a population can be classified into two categories based on frequency of occurrence:

Rare mutations (also called non-recurrent mutations) are defined as those that occur infrequently in populations. Rare mutations are usually recessive and occur in a heterozygous condition so that their effect on the phenotype is not apparent. Rare mutations will usually be lost from populations due to random genetic drift.

Recurrent mutations are defined as those that occur repeatedly and thus can possibly cause a change in gene frequency in populations. For a given locus, the rate of allele A mutating to allele a can be given as the frequency u per generation; a mutates to A at a rate v:

With the frequency of A symbolized as p and that of a symbolized as q, then at equilibrium, pu = qv , or q = p/(u + v) (see the Equation below).

Mutation Rates

Falconer and Mackay (1996) summarize the following key points about mutation rates and their frequency in populations:

• normal spontaneous mutations alone can produce only very slow changes of allele frequency;
• mutation rates are generally quite low for most loci in most organisms, occurring about 10 -5 to 10 -6 per generation or, stated another way, about 1 in 100,000 to 1 in 1,000,000 gametes carry a newly mutated allele at any locus;
• with respect to equilibrium in both directions ( u and v ) in natural populations, forward mutation (from wildtype to mutant; u ) is much more frequent than reverse mutation (from mutant to wildtype; v ); and
• an equilibrium state known as the mutation-selection balance can maintain deleterious alleles at low frequency; selection acts to eliminate deleterious recessive alleles, but very slowly when the allele frequency is low; even if the elimination process of selection is slow, an equilibrium occurs if mutation creates new copies of the deleterious allele.

Somatic vs. Germinal Mutations

Plants are multicellular organisms, but mutation typically starts from a single cell. There are two broad categories of mutations that are classified according to the type of cell tissue involved:

• Somatic mutations occur in somatic tissue, which does not produce gametes. Somatic cells divide by mitosis and therefore through that process, mutations can be passed on to daughter cells. Somatic mutations may have no effect on the phenotype if their function is covered by that of normal cells. However somatic cells that stimulate rapid cell division are the basis for tumors in plants and animals. Somatic mutations usually occur as single events (typically in a single cell) in multicellular organisms or organs that lead to chimera , which is a part of a plant with a genetically different constitution as compared to other parts of the same plant. Somatic mutations are not transmitted to progeny (Fig. 1).
• Germinal or germ-line mutations occur in reproductive cells that produce gametes, and therefore can be passed on to future generations. Germ cells or gametes are formed by meiosis. If a germinal mutation is inherited, then it can be carried in all of the somatic and germ-line cells of the offspring (Fig. 1).

Effects of Mutation on Fitness or Function

Mutations can affect fitness in various ways and can therefore be classified based on their effect on individual fitness:

• Deleterious mutations are those that are harmful and have a negative effect on phenotype, decreasing the fitness of the individual.
• Advantageous mutations are those that are beneficial and have a positive or desirable effect on phenotype, increasing the fitness of the individual.
• Neutral mutations have neither beneficial nor harmful effects.
• Lethal mutations are detrimental and lead to the death of the organism when present.

Mutations can also be classified by their effect on gene function:

• Loss-of-function mutations either result in a gene product that has less function or one that has no function. Phenotypes associated with loss-of-function mutations are usually recessive. Many of the mutations that are associated with crop domestication from wild progenitors involve loss-of-function alleles (Gepts 2002).
• Gain-of-function mutations result in a gene product that has novel function. Altered phenotypes associated with gain-of-function mutations are usually dominant. Many of the changes in crop plants brought about by genetic engineering involve gain-of-function mutations (Gepts 2002).

However, it is important to underscore that not all mutations occur in genes or protein-coding regions of the chromosome, nor do all mutations that do occur in genes lead to altered proteins.

Point vs. Chromosomal Mutations

Mutations are often divided into those that affect a single gene, termed a gene mutation —also sometimes called a point mutation—and those that affect the structure of chromosomes, called a chromosomal mutation . These latter two classes of mutations will be covered in more detail after the concept of gene expression is introduced in the following section.

Point Mutation

Point or gene mutation.

A point mutation is when a single base pair (or just a few) is altered (Fig. 2), an alteration at a “micro” level. There are two general types of point mutations: substitutions or insertions and deletions (the latter two are collectively called INDELs).

Missense Mutations

Some substitution mutations have no effect on the protein coded for. One reason is because of the redundancy of the genetic code (recall that about one fourth of all base pair substitutions code for the same amino acid; such mutations are termed silent mutations since there is no change in amino acid that results from the substitution). Another reason for lack of effect is that even if a change in amino acid occurs (termed missense mutation ), it may have no actual influence on the function of a protein (Fig. 3). Also any mutation located within a non-coding region of the chromosome will not be translated into a protein. Lastly, an altered gene may be masked by other normal copies of the gene present in the genome.

In certain cases, point mutations can have a significant effect—particularly when a substitution produces a stop codon so that the alteration causes the protein synthesis to halt before the protein is entirely translated, altering the entire structure. These are called nonsense mutations (Fig. 3).

Frameshift Mutation

Base pair insertions and deletions are additions (INDELs) or losses of one to several nucleotide pairs in a gene (Fig. 2). Mutations that are insertions and deletions tend to have a much greater effect than do mutations that are base pair substitutions because they disrupt the normal reading frame of trinucleotides. Recall that each group of three bases corresponds to one of 20 different amino acids used to build a protein. Mutations involving base pair insertions and deletions are often therefore referred to as frameshift mutations . Under these circumstances the DNA sequence following the mutation is read incorrectly (Fig. 4).

Chromosomal Mutation

Mutations involving chromosome segments.

Different cells of the same organism and different individuals of the same species generally have the same number of chromosomes, and homologous chromosomes are typically uniform in number and in the arrangement of genes along them. However, mutations can occur that alter the number or structure of chromosomes.

Changes involving chromosomal rearrangements entail the following basic types: deletions , duplications , insertions , inversions , substitutions , and translocations —alterations that occur at a “macro” level (Fig. 5).

• Chromosomal deletions are when loss of a chromosome segment occurs.
• Chromosomal duplications occur when a chromosome segment is present more than once in a genome or along an individual chromosome (Fig. 5). Mutations of this type can involve duplication of chromosome fragments of either noncoding regions or genes that do code for a protein or other gene product. Gene duplications have been important events in the evolution of many crop plants, for example in cotton.Both chromosome deletions and duplications generally result from unequal crossing over during meiosis, whereby one gamete receives a chromosome with a duplicated segment or gene and the other gamete receives a chromosome with a missing or "deleted" segment.
• Chromosomal inversions happen when two breaks occur in a chromosome and the broken segment turns 180°—reversing the orientation of the sequence—and then reattaches (Fig. 5). Such inverted segments may or may not involve the centromere (termed pericentric inversion vs. paracentric inversion ). A consequence of chromosomal inversions is that they either prevent crossing over or if crossing over occurs, the recombinants may be eliminated during meiosis. During meiosis, inverted chromosome segments may form loops in order to pair with the same (non-inverted) sequence on homologous chromosomes.
• Chromosomal insertions (not pictured) and chromosomal substitutions are when gain of an extra fragment of chromosome occurs (Fig. 5).
• Chromosomal translocations entail a change in the location of a chromosome segment. Commonly translocations are reciprocal and thus result from exchange of segments between two non-homologous chromosomes (Fig. 5).

Transpositions

Chromosome segments can also be translocated to a new location on the same chromosome or to a different chromosome but without reciprocal exchange; both of the latter types of mutations are termed transpositions . A transposon (also called a transposable element ) is a DNA element that can move from one location to another. These mobile DNA sequences commonly occur in some genomes and can themselves cause other mutations to occur, depending on where they "transpose".

Mutants and mutations are best known in the context of horror films. In the context of plant breeding and more generally crop production, discuss the consequences of mutations—are they good or bad? Which kinds of mutations are desirable and which ones are undesirable?

Sources of Variation

Sources of genetic variability.

Plant breeding is dependent on differential phenotypic expression. Loci with only one allelic variant (homozygosity) in a breeding population have no effect on the phenotypic variability. Variation can be introduced to breeding populations by various methods:

• hybridization and recombination by sexual reproduction within or between species or populations
• genetic transformation or genetic engineering using recombinant DNA methods
• induced or spontaneous mutations and transposable elements (transposons)
• chromosome manipulation via change in chromosome number and structure ( ploidization ) [to be discussed in the module on Ploidy—Polyploidy, Aneuploidy, Haploidy]
• tissue or cell culture techniques [to be discussed in the module on Ploidy—Polyploidy, Aneuploidy, Haploidy]

CONCEPT OF CROP GENE POOLS

Plant germplasm is a term used to refer to an individual, group of individuals or a clone that represents a genotype, population, or species. With reference to a given crop and its wild and cultivated relatives, the concept of gene pool (all of the genes shared by individuals in a group of interbreeding individuals) has been applied to categorize a broad range of plant genetic resources according to the ease of gene transfer or gene flow to the particular crop species (Harlan and de Wet 1971).

Figure 6 depicts three main categories in the original scheme outlined by J. R. Harlan and J.M.J. de Wet (1971) defined as:

• primary gene pools (GP-1, consisting of biological species that can be intercrossed easily without problems of fertility in the progeny; including both cultivated varieties and wild progenitors of the crop),
• secondary gene pools (GP-2, consisting of more distant relatives that can be intercrossed with difficulty and result in diminished fertility in the hybrids and later generations; including both cultivated and wild relatives of the crop), and
• tertiary gene pools (GP-3, consisting of very distant relatives that can be hybridized with the crop only with special techniques, e.g., embryo rescue, due to problems such as sterility, lethality and other abnormalities),

With the addition of a fourth gene pool that contains synthetic variants and lines with nucleic acid sequences that do not normally occur in nature. Methods of genetic engineering relevant to this fourth gene pool category will be covered briefly in the next section of this module. Examine a more detailed view of the gene pool concept.

Genetic Engineering

Plant transformation, genetic engineering and plant transformation.

Genetic engineering , also referred to as recombinant DNA or rDNA technology or gene splicing , involves moving a DNA segment from one organism into another to 'transform' the recipient or host. Through a broad range of techniques encompassing biotechnology (for example, gene manipulation, gene transfer, cloning of organisms), novel genetic diversity can be generated that extends beyond species boundaries or can be designed and synthesized de novo in molecular laboratories. Potentially, any gene from any species, as well as synthesized segments, could be transferred into a plant using genetic engineering.

Gene transfer can be applied for a variety of objectives:

• Add different or new functions
• Alter existing traits—amplify, suppress, or prevent the expression of a gene already present in the recipient's genome
• 'Tag' and isolate genes in the recipient plant
• Tool for basic gene regulation and developmental studies

Requirements for Gene Transfer

In order to efficiently generate transformants (plants that possess DNA introduced via recombinant DNA technologies), the transformation system used must satisfy several requirements.

• Ability to get DNA into host cells in high concentration to increase the probability of incorporation into the host genome
• Incorporation into the host nucleus (or chloroplast if the objective is to minimize gene flow by pollen or to produce a large quantity of therapeutic proteins)
• Integration into host genome (or stabilization as an autonomous replicon—a plasmid or minichromosome)
• The introduced gene is expressed and translated properly

GENERAL STEPS

• Identification and isolation of a gene that confers a desired trait.
• Introduce the gene into a suitable construct and carrier , such as plasmids or bacterial vectors, for delivery into the host.
• Introduce the DNA into the host.
• Identify and select transformants.
• Regenerate plants.
• Assay for expression of the trait.
• Test for normal sexual transmission, or asexual propagation, of the transferred gene.

The carrier that will deliver the DNA into the host should have certain features.

• Sites in which to insert passenger DNA sequences (gene of interest, plus a selectable marker gene if the gene of interest does not allow for easy selection of transgenic plants).
• Sequences to mediate integration into the host genome
• Selectable marker gene for identification and selection of transformants

Usually, the DNA sequence to be transferred into the host is joined with other sequences to facilitate transfer, incorporation, and expression of the gene. Here's a generalized construct for a T-DNA vector, a carrier derived from an Agrobacterium tumefaciens plasmid (Fig. 1).

The original version of this chapter contained H5P content. You may want to remove or replace this element.

Fig. 1 Diagram of a DNA construct

Introduce the DNA

Several methods are available to introduce the DNA into the host.

• Vector-mediated transfer
• Direct DNA uptake—DNA cannot be taken directly into cells having a cell wall so protoplast must be used.
• Microinjection—DNA is injected directly into the host nucleus
• Acceleration of DNA—coated particles - particles are "shot" into the cell (particle bombardment or gene gun)

Genetic transformation plays an important role in modern-day crop improvement. The first transgenic plant was created in 1983. By 1996, there were already 1.7 million hectares of genetically modified (GM) crops and this number increased 100-fold to 170 million by 2012 and is still increasing. The majority of the GM crops (soybean, corn, cotton, papaya, canola and sugarbeet) were created by the use of Agrobacterium tumefaciens (vector-mediated transfer) to resist either herbicides or insects. Herbicide-resistant crops greatly simplified weed management where mechanization in agriculture is high. Insect-resistant crop plants produce stable yields. The tremendous expansion of GM crop production, however, is not realized without controversies. There is currently an intense public debate over the impact of GM crops on human and animal health. Besides health issues, other concerns surrounding GM crops are whether they can create superweeds by crossing to related weeds, become invasive or cause unintended harm to wildlife.

Bt Gene: Vector Constructs

Let's follow the transfer of a Bt gene into a plant.

The Bt gene was identified and isolated from Bacillus thuringiensis , a bacterium. The gene produces a protein that has toxic effects on Diptera (flies), Lepidoptera (butterflies and moths), and Coleoptera (beetles) species. Soybean was transformed using Agrobacterium tumefaciens . Two different T-DNA vector constructs carrying the Bt gene and one control construct were tested for effectiveness in transforming soybean cells and expressing the Bt toxin (Fig. 7).

Bt Gene: Transformation

Leaf disks from soybean plants were infected and cultured on selective (+) and control (-) media—the selective medium was gradually enriched with an antibiotic (Fig. 8). At time 1, the leaf disks were infected with the respective constructs. Conditions were used to promote callus production and growth. At time 2, the plates were evaluated for calli formation.

Transformed cells are able to develop into calli. These are selected and transferred to a medium containing both antibiotics and growth regulators that promote the formation of shoots and roots (Fig. 9).

Bt Gene: Insect Resistance Evaluation

Plantlets are regenerated and transferred to pots containing sterilized soil. Nearly all of the regenerated plants exhibited normal morphology and vigor. A few had chlorophyll deficiencies— these were eliminated from the study. The remaining regenerated plants were evaluated for insect resistance. An equal number of insect larvae are placed on each regenerated plant; plants are isolated to prevent insects from moving among plants.

Bt Gene: Insect Resistance Data Analysis

After several days, the dead larvae on each plant are counted. Here are the data.

Study the information in the above table. Look for any patterns in the data. Interpret the data by selecting all of the true statements

Advantages and Disadvantages

Although recombinant DNA technologies have some problems, the technologies offer several advantages.

Despite its limitations, plant transformation has additional advantages over conventional breeding. Millions of cells, with regeneration capacity, can be screened for the desired trait in a few weeks. A desired gene can be transferred without the necessity of generations of breeding to move the trait from one line into another. Recombinant DNA technology can also be used to place synthetic genes into plant genomes.

The insertion point of the transferred gene cannot be controlled. Plants contain an estimated 500,000 to 5,000,000 kilobases (kb) of DNA. The maize genome, for example, has more than 4,000,000 kb. Generally, transferred DNA involves relatively small amounts of DNA, on the order of 10 kb, so the insertion ordinarily has little effect on chromosome pairing, recombination, or mitosis. The incorporated gene may or may not affect other genes in the recipient genome, depending on where it inserts.

• In a non-coding region—no effect on the recipient genome
• In a gene that occurs in multiple copies in the genome—the effect, if any, is usually not detectable.
• In a single-copy gene—inactivates or alters the expression of the single-copy gene.

Insertion into a single copy gene is rare. If it does insert into a single-copy gene, inactivation or alteration of the expression of the disrupted gene may be undetected, or may cause favorable or adverse results—albino and other chlorophyll deficiencies are common problems.

Why do these characters generally exhibit dominant, single-gene inheritance? Traits acquired via gene transfer often add a function to the transformed plant. Because the transferred trait is unique in the transformant's genome, the transformant does not possess any contrasting alleles for the character. Thus, its inheritance is expected to be dominant and as a single gene.

Gene transfer is also used to suppress or eliminate, or to amplify the expression of genes already possessed by the host plant. These are also usually designed to behave as dominants.

Expression involves many steps, all of which must occur properly to obtain the desired phenotype. Expression must be appropriately regulated.

• Generation of the gene's product-requires proper transcription, mRNA processing, and translation.
• Location of expression must be in the appropriate plant part.
• Timing of expression needs to occur at the right stage of the plant's development.
• Amount needs to be at an effective level or extent of expression to generate the desired phenotype.

The Module Reflection appears as the last "task" in each module. The purpose of the Reflection is to enhance your learning and information retention. The questions are designed to help you reflect on the module and obtain instructor feedback on your learning. Submit your answers to the following questions to your instructor.

• In your own words, write a short summary (< 150 words) for this module.
• What is the most valuable concept that you learned from the module? Why is this conceptvaluable to you?
• What concepts in the module are still unclear/the least clear to you?

Collard, B.C.Y., M.Z.Z. Jahufer, J.B. Brouwer, and E.C.K. Pang. 2005. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142: 169-196.

Falconer, D.S. and T.F.C. Mackay. 1996. Introduction to Quantitative Genetics. 4th edition. Longman Pub. Group, Essex, England.

Harlan, J.R., and J.M.J de Wet. 1971. Towards a rational classification of cultivated plants. Taxon 20: 509-517.

Gepts, P. 2002. A comparison between crop domestication, classical plant breeding, and genetic engineering. Crop Science 42: 1780-1790.

Nageswara-Rao, M. and J.R. Soneji. 2008. Molecular genetic markers: what? why? which one for exploring genetic diversity? Perspectives-The Science Advisory Board, 8 September 2008 [available online September 23, 2011, http://www.scienceboard.net/community/perspectives.210.html ].

Neuffer, M.G, E.H. Coe, and S.R. Wessler. 1997. Mutants of Maize. Cold Springs Harbor Laboratory Press.

NIH-NHGRI (National Institutes of Health. National Human Genome Research Institute). 2011. Talking Glossary of Genetic Terms. [available online February 6, 2017, http://www.genome.gov/glossary/ ].

Amino acids are the building blocks of polypeptides , proteins , and enzymes . The order of the nucleotides on a strand of RNA, as transcribed from DNA , determines the order of amino acids in a polypeptide. Each group of three consecutive nucleotides of the RNA codes for a particular amino acid, or the beginning or end of the message. These triplets of nucleotides are called codons (Fig. 20).

The genetic code, or instructions from a gene that direct the cell to make a specific protein, is usually based on the messenger RNA (mRNA) sequence (Fig. 21). In mRNA, uracil (U), rather than thymine (T), is the nucleotide base that complements adenine (A) on the DNA strand; guanine (G) complements cytosine (C) in both DNA and RNA.

Here are some examples of codons:

• A-A-A and A-A-G signal the amino acid lysine (Lys)
• G-A-A and G-A-G code for glutamine (Gln)
• A-U-G signals the start of a coding sequencing and codes for methionine (Met)
• U-A-A, U-A-G, and U-G-A are stop codons.

Of the 64 possible combinations of three bases, 61 specify an amino acid, while the remaining three combinations are stop codons , or trinucleotide sequences that indicate the end of the message, terminate translation of that mRNA section, and signal a “stop” to protein synthesis (Fig. 22).

The portion of a DNA molecule that, when translated into amino acids, contains no stop codons is referred to as an open reading frame (Fig. 23).

Acknowledgements

This module was developed as part of the Bill & Melinda Gates Foundation Contract No. 24576 for Plant Breeding E-Learning in Africa.

Crop Genetics, Mutations and Variation - Authors: Laura Merrick, Arden Campbell, Deborah Muenchrath, and Shui-Zhang Fei (ISU)

Multimedia Developers: Gretchen Anderson, Todd Hartnell, and Andy Rohrback (ISU)

How to cite this module: Merrick, L., A. Campbell, D. Muenchrath, and S. Fei. 2016. Mutations and Variation. In Crop Genetics, interactive e-learning courseware. Plant Breeding E-Learning in Africa. Retrieved from https://pbea.agron.iastate.edu

A type of reproductive cell that leads to the production of gametes. In plants, male germ cells are contained within pollen grains and female germ cells are contained within ovules.

This is the question that is trying to be proven incorrect . Usually, this occurs when trying to prove some treatment causes a significant difference from expected. Opposite of alternative hypothesis .

Self-pollination in plants.

Enzymes are organic catalysts. They cause chemical reactions to occur rapidly which would otherwise occur very slowly or not at all. Most of them are very specific— for example, urease catalyzes the hydrolysis of urea. Many enzymes require a metallic cofactor to work with them. Also, enzymes are large complex of globular proteins consisting of one or more polypeptide chains.

Chapter 8: Inheritance of Quantitative Traits Copyright © 2023 by Laura Merrick; Kendra Meade; Arden Campbell; Deborah Muenchrath; Shui-Zhang Fei; and William Beavis is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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DNA and histone modifications undergo extensive reprogramming to reset the epigenomic state between generations. This Review discusses the mechanisms and consequences of epigenetic reprogramming during plant sexual reproduction. Despite various similarities to the equivalent process in mammals, a key difference is the seemingly incomplete reprogramming of plant epigenomes, which has implications for evolution and transgenerational epigenetic inheritance.

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•  &  Frédéric Berger

Research Highlight | 24 June 2014

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Review Article | 18 November 2013

Evolution of crop species: genetics of domestication and diversification

The recent improvement in technologies to identify genetic variants linked with quantitative traits has allowed the identification of variants that are associated with crop domestication. This Review considers these approaches and their application to the study of crop domestication.

• Rachel S. Meyer
•  &  Michael D. Purugganan

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Tunable plastic development

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Review Article | 09 October 2013

Advanced genetic tools for plant biotechnology

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• Wusheng Liu
• , Joshua S. Yuan
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Food security is confronted by great threats from crop yield stagnation; growing demand for high quality crops; and global climate change in the form of frequent extreme weather events, increasing occurrence of various pests and diseases, and shortage of water and other resources. The benefits of genetic studies of crop breeding globally are thought to be imperative for realizing high-yielding crop production. Breeding practices for modern crops are critical in facing these challenges, and a deep understanding of these practices is crucial to uncover how to identify genetic improvements for productive crops. This Special Issue explores the basic mechanism of the breeding of high yield and high-quality crops. It is expected that development and integration of new technology into established breeding schemes will contribute significantly to crop genetic improvements, for instance, with the breeding of varieties with better quality and improved disease resistance, improving yield potential, and enhancing resistance or tolerance to biotic stress such as drought and heat. This Special Issue gives priority to articles that are of direct relevance to plant breeders, inviting original research, technology reports, opinion articles, perspectives, reviews, and mini reviews. This publication also welcomes studies on cereals, beans, potatoes, fiber, oilseeds, sugar, seasoning, medicinal, green manure, and aromatic crops.

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• Published: 28 March 2013

Open questions: Reflections on plant development and genetics

• Virginia Walbot 1  

BMC Biology volume  11 , Article number:  25 ( 2013 ) Cite this article

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Genomics has given us a new appreciation for the many ways genes and genomes evolve

At the turn of the millennium, the most we could hope for was a few small genomes completed and hordes of ESTs and genomic snippets from most species. What a difference a decade makes. Now that whole genome sequencing is routine and there are sufficient numbers of plant genomes with near complete assemblies with strong support, it will be routine to ‘paste on’ the genomes of related genera and even families. Furthermore, using the multiple (hundreds or thousands) of distinct genomes of inbred lines or ecotypes, allelic diversity and micro-heterogeneity in chromosome organization can be analyzed directly. These assemblages can be used to test ideas about genome rearrangement, the timing and extent of transposon amplification, gene duplications and losses, and both promoter region and transcription unit conservation and change. But has genomics explained development or physiology?

Genetics still has a role because mechanistic insights emerge from detailed analysis of a particular organism and with genomics we can tackle very difficult phenotypes

Despite the success of genomics, biological details are best viewed by close observation and analysis of a single species. In the context of all genes in that organism, what is the impact of mutation in one gene? What are the details of biochemical and gene expression regulation in the cells of this particular plant under specific environmental conditions? In the past 10 years, genomics has given us the ability to observe and dissect the small accretions in phenotype typical of multi-locus traits. We have unprecedented access to analysis of quantitative traits, that is, how a few or even dozens of specific allelic combinations at many loci add up to a particular trait such as days to flowering or ability to resist salt damage. Of course, classical genetics found the major players - lethal mutations always prove that something is essential - but the small number of major players do not explain the endless variety of intermediate types and just slightly more somatically and reproductively fit individuals under specific conditions.

Real plants live in a variable environment and integrate environmental conditions into developmental decisions

As a corn geneticist, I’ve always faced the variability of growing conditions - day length, temperature, winter Hawaii compared to summer California, and so on. We observe phenotypic plasticity all the time, and now we are joined by those studying ‘growth chamber’ species who have begun to aggressively and effectively assess phenotypes in natural environments and diverse ecotypes in common gardens. The unprecedented combination of genome markers and high-throughput phenotyping is inspiring a new generation of ecophysiologists. It has been nearly 75 years since Clausen, Keck and Hiesey of Stanford/Carnegie Institution began publishing their common garden experiments that established that ecotypes differ genetically and that plants can show significant acclimations in form.

This latter point was noted by Charles Darwin, with particular reference to reproduction in his book The Different Forms of Flowers on Plants of the Same Species [ 1 ]. Typical open pollinated species that suffer low seed set can subsequently switch to making cleistogamous (closed bud), self-fertile flowers to ensure reproduction. It is endlessly fascinating to me how well plants cope with a variable environment and can, for example, produce a succession of leaves of different phenotypes to avoid sun, wind or other damage. Although the common wisdom is that physiology feeds into development by fine-tuning organ growth, our own work points to a deeper connection in that hypoxia is the regulator of the differentiation of maize anther cells competent for meiosis [ 2 ] and provides an example of a direct connection between cell fate setting and environmental conditions.

The meaning of stem cells and the fundamental differences between plants and animals

Since the 1950s it has been clear that some individual adult plant cells can regenerate an entire organism. This was thought impossible in multicellular animals, but in the past few years it has become clear that animal stem cells can be reprogrammed in vitro to exhibit pluripotency or totipotency. But this is not easy! Why can plant cells dedifferentiate and redifferentiate autonomously, without a somatic niche helper cell population or an onslaught of special factors applied exogenously? One could argue that even a large tree is just cells that are currently cooperating to make a larger organism but that most of the cells retain a somewhat ‘single cell’ perspective on survival. The absence of a plant germ line may be the fundamental feature that divides plants from animals and may in ways we will ultimately understand determine the plasticity of plant cells within a complex multicellular organism. There is no doubt that a typical animal stem cell is so much more limited in what it can do, or does do in the body, than a shoot apical meristem cell from a flowering plant. In effect the plant stem cells are building entire new organs all the time - new limbs, new trunk - solving polarity issues and the other key developmental decisions that are resolved in animal embryos. Furthermore, in the very act of generating a new leaf, the shoot apex not only regenerates itself but it makes an axillary meristem, doubling the growth potential of the organism.

A favorite thought of mine is that this vegetative diversification of growing points - a kind of distributive growth - permits plants the luxury of mutation and an immediate assessment of fitness vegetatively. Animals sequester their germ line and stem cells to prevent mutation while plants may allow, even promote through activation of transposons, genome mutation. Novelty such as bud sports is the foundation for viticulture and tree crop diversification, evidence that some somatic mutations are highly favorable. And then when apices switch to making flowers the more successful branches will make more flowers and hence have the potential to make more offspring inheriting a ‘pre-tested’ allele that confers novel somatic properties. As a bonus, with gametophytic selection acting on the haploid phase of the life cycle, many highly deleterious mutations are eliminated from the plant gene pool, curtailing an increase in genetic load.

In summary, the past decade has provided many exciting scientific advances and a few solutions to long-standing questions. Yet the frontier of unanswered questions is still vast, and the challenge is to marshal our new resources to design appropriate and clever (and in these times, economical) approaches to resolving the mechanisms underlying the fundamental properties of the green world.

Darwin C: The Different Forms of Flowers on Plants of the Same Species. 1877, New York: D Appleton and Co.

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Kelliher T, Walbot V: Hypoxia triggers meiotic fate acquisition in maize. Science. 2012, 337: 345-348. 10.1126/science.1220080.

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Walbot, V. Open questions: Reflections on plant development and genetics. BMC Biol 11 , 25 (2013). https://doi.org/10.1186/1741-7007-11-25

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• v.8(4); 2017

The impact of Genetically Modified (GM) crops in modern agriculture: A review

Ruchir raman.

Faculty of Science (School of Biosciences), The University of Melbourne, Parkville, VIC 3010, Australia

Genetic modification in plants was first recorded 10,000 years ago in Southwest Asia where humans first bred plants through artificial selection and selective breeding. Since then, advancements in agriculture science and technology have brought about the current GM crop revolution. GM crops are promising to mitigate current and future problems in commercial agriculture, with proven case studies in Indian cotton and Australian canola. However, controversial studies such as the Monarch Butterfly study (1999) and the Séralini affair (2012) along with current problems linked to insect resistance and potential health risks have jeopardised its standing with the public and policymakers, even leading to full and partial bans in certain countries. Nevertheless, the current growth rate of the GM seed market at 9.83–10% CAGR along with promising research avenues in biofortification, precise DNA integration and stress tolerance have forecast it to bring productivity and prosperity to commercial agriculture.

INTRODUCTION

Genetic modification (GM) is the area of biotechnology which concerns itself with the manipulation of the genetic material in living organisms, enabling them to perform specific functions. 1 , 2 The earliest concept of modification for domestication and consumption of plants dates back ∼10,000 years where human ancestors practiced “selective breeding” and “artificial selection” – the Darwinian-coined terms broadly referring to selection of parent organisms having desirable traits (eg: hardier stems) and breeding them for propagating their traits. The most dramatic alteration of plant genetics using these methods occurred through artificial selection of corn – from a weedy grass possessing tiny ears and few kernels (teosinte; earliest recorded growth: central Balsas river valley, southern Mexico 6300 years ago) to the current cultivars of edible corn and maize plants (Doebley et al., 2016, Fig 1 ). The use of similar techniques has also been reported to derive current variants of apples, broccoli and bananas different from their ancestral plant forms which are vastly desirable for human consumption. 3

The evolution of modern corn/maize (top) from teosinte plants (bottom) by repetitive selective breeding over several generations. [Sources: 50 (top figure), 51 (bottom figure)].

The developments leading to modern genetic modification took place in 1946 where scientists first discovered that genetic material was transferable between different species. This was followed by DNA double helical structure discovery and conception of the central dogma – the transcription of DNA to RNA and subsequent translation into proteins – by Watson and Crick in 1954. Consequently, a series of breakthrough experiments by Boyer and Cohen in 1973, which involved “cutting and pasting” DNA between different species using restriction endonucleases and DNA ligase – “molecular scissors and glue” (Rangel, 2016) successfully engineered the world's first GM organism. In agriculture, the first GM plants – antibiotic resistant tobacco and petunia – were successfully created in 1983 by three independent research groups. In 1990, China became the first country to commercialise GM tobacco for virus resistance. In 1994, the Flavr Savr tomato (Calgene, USA) became the first ever Food and Drug Administration (FDA) approved GM plant for human consumption. This tomato was genetically modified by antisense technology to interfere with polygalacturonase enzyme production, consequently causing delayed ripening and resistance to rot. 4 Since then, several transgenic crops received approvals for large scale human production in 1995 and 1996. Initial FDA-approved plants included corn/maize, cotton and potatoes ( Bacillus thuringiensis (Bt) gene modification, Ciba-Geigy and Monsanto) canola (Calgene: increased oil production), cotton (Calgene: bromoxynil resistance) and Roundup Ready soybeans (Monsanto: glyphosate resistance), 4 Fig 2 ). Currently, the GM crop pipeline has expanded to cover other fruits, vegetables and cereals such as lettuce, strawberries, eggplant, sugarcane, rice, wheat, carrots etc. with planned uses to increase vaccine bioproduction, nutrients in animal feed as well as confer salinity and drought resistant traits for plant growth in unfavourable climates and environment. 4 , 2

A timeline of events leading to the current GM crop era.

Since their commercialisation, GM crops have been beneficial to both economy and the environment. The global food crop yield (1996–2013) has increased by > 370 million tonnes over a relatively small acreage area. 2 Furthermore, GM crops have been recorded to reduce environmental and ecological impacts, leading to increases in species diversity. It is therefore unsurprising that GM crops have been commended by agricultural scientists, growers and most environmentalists worldwide.

Nevertheless, advancements in GM crops have raised significant questions of their safety and efficacy. The GM seed industry has been plagued with problems related to human health and insect resistance which have seriously undermined their beneficial effects. Moreover, poor science communication by seed companies, a significant lack of safety studies and current mistrust regarding GMOs have only compounded problems. These have led many countries, particularly the European Union and Middle East to implement partial or full restrictions on GM crops. GM agriculture is now widely discussed in both positive and negative frames, and currently serves as a hotbed of debate in public and policymaking levels.

CHALLENGES IN COMMERCIAL AGRICULTURE

The agriculture industry has been valued at an estimated US$ 3.2 trillion worldwide and accounts for a large share of the GDP and employment in developing and underdeveloped nations. 5 For instance: Agriculture contributes only 1.4% towards the GDP and 1.62% of the workforce in US in comparison with South Asian regions, where it contributes 18.6% towards the GDP and 50% of the workforce. 6 However, despite employing nearly 1 in 5 people worldwide (19% of the world's population), 7 the agriculture industry is projected to suffer significant global setbacks (population growth, pest resistance and burden on natural resources) by 2050, which has been elaborated further in this section.

Explosive Population Growth

The Food and Agricultural Organisation projects the global population to grow to approximately 9.7 billion by 2050 – a near 50% increase from 2013 – and further to an estimated 11bn by 2100. Current agricultural practices alone cannot sustain the world population and eradicate malnutrition and hunger on a global scale in the future. Indeed, the FAO also estimates that despite a significant reduction in global hunger, 653 mn people will still be undernourished in 2030. 8 Additionally, Ray et al. and other studies depict the top four global crops (soybean, maize, wheat and rice) are increasing at 1.0%, 0.9%, 1.6% and 1.3% per annum respectively– approximately 42%, 38%, 67% and 55% lower than the required growth rate (2.4%/annum) to sustain the global population in 2050. 9 Compounded with other problems such as improved nutritional standards in the burgeoning lower-middle class and projected loss in arable land (from 0.242 ha/person in 2016 to 0.18 ha/person in 2050) 2 due to degradation and accelerated urbanization, rapid world population expansion will increase demand for food resources.

Pests and Crop Diseases

Annual crop loss to pests alone account for 20–40% of the global crop losses. In terms of economic value, tackling crop diseases and epidemics and invasive insect problem costs the agriculture industry approximately $290 mn annually. 8 Currently, major epidemics continue to plague commercial agriculture. It has been projected that crop disease and pest incidences are expanding in a poleward direction (2.7 km annually), 10 indicated by coffee leaf rust and wheat rust outbreaks in Central America. These incidences have largely been attributed to an amalgamation of globalisation leading to increased plant, pest and disease movement, increase in disease vectors, climate change and global warming. 8

While integrated pest management and prevention techniques somewhat mitigate the pest problem, they are insufficient to tackle the transboundary crop-demics. The epidemiology of the Panama disease (or Panama wilt), caused by the soil fungus Fusarium oxysporum f.sp. cubense (Foc) 11 provides solid evidence in this regard. Since the early-mid 1990s the Tropical Race-4 (TR4) strain, a single pathogen Foc fungus clone, has significantly crippled the global banana industry. In 2013, the Mindanao Banana Farmers and Exporters association (in Philippines) reported infection in 5900 hectares of bananas, including 3000 hectares that were abandoned. In Mozambique, symptomatic plants currently account for >20% of total banana plantations (570,000 out 2.5m) since the reporting of TR4 in 2015. Additionally, TR4 losses have cost Taiwanese, Malaysian, and Indonesian economies a combined estimate of US$ 388.4 mn. 12 Therefore, an alarming increase in transboundary crop and pest diseases have broad environmental, social and economic impacts on farmers and threaten food security.

Burden on Natural Resources

The FAO's 2050 projections suggest projected natural resource scarcities for crop care. 8 Despite overall agricultural efficiency, unsustainable competition has intensified due to urbanisation, population growth, industrialisation and climate change. Deforestation for agricultural purposes has driven 80% of the deforestation worldwide. In tropical and subtropical areas where deforestation is still widespread, agricultural expansion accounted for loss of 7 million hectares per annum of natural forests between 2000–2010. 8 Additionally, water withdrawals for agriculture accounted for 70% of all withdrawals, seriously depleting natural water resources in many countries. This has particularly been observed in low rainfall regions, such as Middle East, North Africa and Central Asia where water for agriculture accounts for 80–90% 8 of the total water withdrawal. These trends are predicted to continue well into the 21st century and therefore increase the burden of natural resource consumption globally.

SOLUTIONS PROVIDED BY GM CROPS

GM crops have been largely successful in mitigating the above major agriculture challenges while providing numerous benefits to growers worldwide. From 1996–2013, they generated $117.6 bn over 17 years in global farm income benefit alone. The global yearly net income increased by 34.3% in 2010–2012. 13 , 14 Furthermore, while increasing global yield by 22%, GM crops reduced pesticide (active ingredient) usage by 37% and environmental impact (insecticide and herbicide use) by 18%. 15 To achieve the same yield standards more than 300 million acres of conventional crops would have been needed, which would have further compounded current environmental and socioeconomic problems in agriculture. 2

To further emphasise the impact of GM crops on economies: two case studies – GM Canola (Australia) and GM cotton (India) – have been highlighted in this review.

GM Cotton (India)

In India, cotton has served as an important fibre and textile raw material and plays a vital role in its industrial and agricultural economy. Nearly 8 million farmers, most of them small and medium (having less than 15 acres of farm size and an average of 3–4 acres of cotton holdings) depend on this crop for their livelihood. In 2002, Monsanto-Mahyco introduced Bollgard-I, India's first GM cotton hybrid containing Cry1Ac -producing Bacillus thuringiensis ( Bt ) genes for controlling the pink bollworm ( P. gossypiella ) pest. 16 Initially, only 36% of the farmers adopted the new crop however this statistic soon grew to 46% in 2004 17 after Bt- cotton was approved nationwide. This was followed by approval and launch of Bollgard-II (a two-toxin Cry1Ac and Cry2Ab -producing Bt- pyramid conferring resistance to bollworm) by Monsanto-Mahyco, which subsequently enhanced Bt- cotton adoption among Indian cotton growers ( Fig 3 ).

Adoption of GM canola (top) and GM cotton (bottom) in Australia and India respectively. The primary vertical axis shows the total acreage of cotton and canola along with the proportion of GM and non-GM crops grown per year, while the secondary horizontal axis depicts the percentage of GM crop adoption among farmers and growers per year. (Sources: 22 , 18 ).

Despite controversies, Bt -cotton's implementation has largely benefited Indian farmers and agricultural economy. Bt -cotton has increased profits and yield by Rs. 1877 per acre (US$38) and 126 kg/acre of farmland respectively, 50% and 24% more than profit and yield by conventional cotton. This translates to a net increase of Bt -cotton growers' annual consumption expenditures by 18% (Rs. 15,841/US$321) compared to non-adapters, highlighting improved living standards. 17 Bt -cotton adoption has also resulted in a 22-fold increase in India's agri-biotech industry due to an unprecedented 212-fold rise in plantings from 2002–2011 (accounting for ∼30% of global cotton farmland), surpassing China and making it a world leading grower and exporter. 7 million out of the 8 million farmers (88%) are growing Bt-cotton annually. Cotton crop yields have also increased 31% while conversely insecticide usage has more than halved (46% to 21%) enhancing India's cotton income by US$11.9 bn. 18 Therefore, Bt- cotton has resulted in economic prosperity among Bt -cotton growers, with 2002–11 often being called a white gold period for India's GM cotton industry.

GM Canola (Australia)

Canola in Australia is grown as a break crop, providing farmers a profitable alternative along with rotational benefits from continuous cereal crop phases and their related weed/pest mechanisms. Other benefits include broadleaf weed and cereal root disease control and better successive cereal crop growth. It is most prominently grown in Western Australia (WA), where it accounts for 400–800,000 ha of farmland and is the most successful of four break crops (oat, lupin, canola and field pea). From 2002–2007, Canola production in WA alone accounted for a yield of 440 mn tonnes valued at A$200mn. 19 Nevertheless Canola has been a high risk crop and particularly susceptible to blackleg disease (caused by fungus Leptosphaeria maculans ), and weeds such as charlock ( Sinapis arvensis ), wild radish ( Raphanus raphanistrum L) and Buchan ( Hirschfeldia incana (L.) Lagr.-Foss) which increase anti-nutritional compound content and composition in canola oil, degrading quality. 20

In 2008–09, two herbicide resistant GM canola varieties: Roundup Ready® (Monsanto) and InVigor® (Bayer Cropsciences) were introduced in Australia. Roundup Ready® contained gene variants with altered EPSP synthase (5-enolpyruvylshikimate-3-phosphate) alterations along with a glyphosate oxidoreductase gene making it glyphosate resistant. It gained OGTR approval after trials showed its environmental impact was less than half (43%) of triazine tolerant canola varieties 21 , 19 and remains the only OGTR-approved GM canola till date. The introduction of Roundup Ready® canola has had a positive impact on farmers by controlling weeds that were erstwhile difficult to mitigate. In 2014, GM canola planting area (hectares) was up to 14% in 2014 from just 4% in 2009 ( Fig 3 ), representing a near three-fold increase and contributing to Australia's growing biotech crop hectarage. This increase was more notable in WA, where GM canola was planted from 21% canola farmers in 2014, up from 0% in 2009. 22 This has led to more research and development of different canola varieties to improve oil content and quality, yield and maturity. 20

PROBLEMS AND CONTROVERSIES

Although a successful technology, GM crop use has been controversial and a hotbed for opposition. Their public image has been severely impacted leading to full or partial bans in 38 countries including the European Union ( Fig 4 ). This section highlights major controversies and reflects on some real problems faced by commercialised GM crops.

The figure depicts the current acceptance of GM crops in different countries. Green: National bans. Yellow: Restrictive laws, Red: No formal laws (Source: 52 ).

Monarch Butterfly Controversy (1999)

The Monarch butterfly controversy relates Losey et al.’s publication in Nature wherein they compared Monarch butterfly ( Danaus plexippus ) larval feeding cycle of milkweed ( Asclepias curassavica) dusted with N4640- Bt maize pollen to a control (milkweed dusted with untransformed corn pollen). They observed the N4640- Bt reared larvae to eat lesser, grow slower and have higher mortality and predicted N4640- Bt maize to have significant off target effects and significantly impact Monarch populations due to the following reasons:

• • Monarch larvae's main nutrition is derived from milkweed, which commonly occurs in and around the corn field edges.
• • Maize pollen shedding coincides with monarch larval feeding cycles during seasonal summer.
• • ∼50% of the Monarch population is concentrated within the US maize belt during summer, a region known for intense maize production. 23

Losey et al. ’s conclusions were challenged by academics for improper experimental design and validity and soundness of extrapolating laboratory assays to field testing. There were many subsequent studies performed, depicting Bt- maize to be highly unlikely to affect Monarch population. For instance: Pleasants et al., 24 reasoned that several factors, most notably rainfall (reducing pollen by 54–86%) and leaf pollen distribution (30–50% on upper plant portions/preferred larval feeding sites) reduced larval exposure to Bt- maize pollen 24 and Sears et al., 25 argued that Bt- maize production, should it rise to ∼80% would only affect 0.05%-6% monarch population. 25

Nevertheless, Losey et al. ’s results garnered acclaim in the press for raising both the public's and biotech companies' consciousness about possible off-target Bt- maize on monarch butterflies. However further attempts to extrapolate their results to other Bt and GM crops have been unsuccessful, with current evidence suggesting effectiveness in insect control without off-target effects. 25

The Séralini Affair (2012)

The Séralini affair concerns itself with a controversial GM crop study by Gilles-Éric Séralini in Springer during 2012–14. The original paper published in 2012 studied the effect of NK-603 Roundup Ready® Maize (NK-603 RR Maize) on rats. It used the same experimental setup as an earlier Monsanto safety study to gain maize approval 26 and reached the following observations:

• • Significant chronic kidney deficiencies representing 76% of altered parameters.
• • 3–5x higher incidence of necrosis and liver congestions in treated males.
• • 2–3-fold increase in female treatment group mortality.
• • High tumour incidences in both treated sexes, starting 600 days earlier than control (only one tumour noted in control).

The 2012 study attributed observations to EPSPS overexpression in NK-603 RR Maize, found the Monsanto study conclusions “unjustifiable” and recommended thorough long-term toxicity feeding studies on edible GM crops. 27 The paper divided opinion, with Séralini being framed as both as a hero of the anti-GM movement and as an unethical researcher. His paper drew heavy criticism for its flawed experimental design, animal type used for study, statistical analysis and data presentation deficiencies and overall misrepresentations of science and was retracted (Arjó et al., 2012,. 28 In 2014, Séralini republished his nearly-identical study in expanded form which since continues to fuel the GM crop debate.

GM Crops: An Imperfect Technology

Despite the above controversies being proven unfounded, GM crops are an “imperfect technology” with potential major health risks of toxicity, allergenicity and genetic hazards associated to them. These could be caused by inserted gene products and their potential pleiotropic effects, the GMO's natural gene disruption or a combination of both factors. 4 , 2 The most notable example of this is Starlink maize, a Cry9c- expressing cultivar conferring gluphosinate resistance. In the mid-1990s, the USDA's Scientific Advisory Panel (SAP) classified Cry9c Starlink as “potentially allergenic” due to its potential to interact with the human immune system. In 1998, the US Environment Protection Agency (EPA) granted approval for Starlink's use in commercial animal feed and industry (eg: biofuels) but banned it for human consumption. Following this, relatively small Starlink quantities (∼0.5% of the US corn acreage) were planted between 1998–2000. 29 , 30 In 2000, Starlink residues were detected in food supplies not only in USA but also EU, Japan and South Korea where it completely banned. Furthermore, the EPA received several adverse allergic event reports related to corn, prompting a worldwide Starlink recall. About 300 different maize products were recalled in US alone by Kellogg's and Mission Foods. Starlink inadvertently affected ∼50% of US maize supply and depressed US corn prices by 8% for CY2001. 31

Another problem faced by GM crops currently is pest resistance due to gene overexpression leading to pest evolution via natural selection. Indeed, an analysis of 77 studies' results by Tabashnik et al. depicted reduced Bt- crop efficacy caused by field evolved pest resistance for 5 out of 13 (38.4%) major pest species examined in 2013, compared to just one in 2005, 32 Table 1 ). Furthermore, such resistance can be evolved over several generations in a relatively short time as most insects have shorter life spans. In maize, S.frugiperda and B.fusca resistance was reported after just 3 and 8 years respectively, consistent with the worst case scenarios. In the former, it led to crop withdrawal in Puerto Rico and was reported to still affect maize growers in 2011, 4 years after crop withdrawal. In India, P. gossypiella resistance currently affects ∼90% Bollgard-II Bt- hybrid cotton growers and ∼35% (4 million ha) of cultivable cotton area in key regions. 32 , 33

Crops reported with >50% pest resistance and reduced efficacy.

1- Time to first reported resistance of pest to GM plant. 2-Toxin secreted by affected GM plant.

To mitigate the problems regarding GM technologies, a series of strict regulatory measures have been proposed to prevent cross-contamination of split-approved GM crops banned for human consumption. These include implementation and enforcement buffer zones to prevent cross contamination of crops, better laboratory testing to confirm adverse allergic event cases and an overall inclusion of stakeholders and representatives in policymaking and communication. 30 Additionally, Bt pest resistance could be controlled by implementation of high-dose Bt toxin standards in transgenic crops and evaluation of insect responses, integration of Host plant resistance (HPR) traits in cultivars to control secondary pests, 34 preparation of abundant non- Bt plants refuges near Bt crops and proactive implementation of two-toxin Bt- pyramids producing ≥ 2 distinct toxins against as single pest species. 32 These suggested measures in pest management and regulation if implemented could help the agriculture industry overcome the imperfect problems of GM crops while significantly regaining public trust of this technology.

GM AGRICULTURE: TRENDS AND FUTURE AVENUES

The GM seed market has changed drastically since 1996 from a competitive sector owned by family owners to one of the fastest growing global industries dominated by a small number of corporations. Analysts predict a Compounded Annual Growth Rate (CAGR) between 9.83–10% between 2017–2022 for this industry where it is projected to reach US$113.28 bn, an approximately four-fold increase from US$26.7 bn in 2007, 35 , 36 MarketWatch, 2016). This has been attributed to a rising biofuel adoption in lieu of conventional fuels in Asia-Pacific (APAC) and Africa, leading to increase plantings of energy crops (wheat, sugarcane, corn and soybean) for production. Nevertheless, despite growth spikes in APAC and Africa, North America currently dominates the GM seed industry with a market share of ∼30%, and is forecast to do so in 2020 (MarketWatch, 2017).

The GM seed market has currently been consolidated by the “big five” companies: Monsanto, Bayer CropScience, Dupont, Syngenta and Groupe Limagrain ( Table 2 ). As of 2016, they account for 70% of the market (up from ∼60% in 2009). 37 , 38 The “big five” players are currently acquiring and forming joint ventures with smaller firms and competitors on a transnational scale, serving as strong entry barriers in this industry. 36 Since 2016, major ongoing Mergers and Acquisitions (M&As): Syngenta's takeover by ChemChina (completed June 2017- US$43 bn), 39 Bayer-Monsanto merger (ongoing- $66bn) 40 and Dow-Dupont merger (∼US$140 bn- antitrust approval) 41 have been happening in the industry. Only time will determine how these M&As impact the industry, growers and consumers.

A snapshot of the “big five” GM seed companies.

1 – Converted from EUR at current NASDAQ rates (July 2017), 2 – Ongoing Merger/Acquisition, 3- Completed Merger/Acquisition, 4- Public non-quoted company, 5- Sourced from Hoovers D&B, 2017, 6 – In this case, market share represents global market share and market capitalisation is local.

The latest reports indicate that the agriculture industry invests around $69 billion globally on its Research and Development (R&D). 42 This investment has fuelled research many emerging avenues for GM crop technology. However, innovation has strictly been influenced by the “big five” due to broad patent claims, and high research, legal and development costs for patent eligible products. For instance, the top 3 seed companies controlled 85% transgenic and 70% non-transgenic corn patents in USA in 2009. 36

In the GM seed market, R&D is currently occurring in the conventional areas of insect resistance, increased crop yield and herbicide tolerance. Increasing R&D has also been invested on precision site-directed nuclease techniques (CRISPR, ZFNs and TALENs) for desired gene integration in host plants. 14 , 43 Current studies show negligible/zero off target mutations (Schnell et al., 2015,. 44 This is starkly contrasting to conventional breeding techniques which are often associated with undesired alteration risks via linkage drag and random, unspecified mutations. 45 Additionally, biofortification and stress tolerance have been identified as areas for future GM seed research. Both fields are currently of major interest with a growing body of scientific studies. They tackle key problems: while biofortification addresses malnutrition and micronutrient deficiency; stress tolerance addresses biodegradation, climate change and shrinking cultivable area. Since the development of Vitamin-A biofortified rice in 2000, 46 studies highlight further extrapolation in enhancing human diet using biofortifications, with recorded success in iron and zinc. 47 Moreover, recent genetic modification studies in Arabidopsis and Barley have depicted adaptation to stress tolerance and biomass growth in adverse conditions (Mendiondo et al., 2016,. 48 Three stress-tolerant corn hybrids [Pioneer Optimum AQUAmax™ (Dupont Pioneer), Syngenta Artesian™ (Syngenta) and Genuity™ DroughtGard™ (Monsanto)] are currently being marketed for drought resistance, 49 showcasing enormous potential for economic profitability in the above areas.

GM crops can mitigate several current challenges in commercial agriculture. Current market trends project them as one of the fastest growing and innovative global industries, which not only benefit growers but also consumers and major country economies. However, it is imperative that the agricultural industry and science community invest in better science communication and regulation to tackle unethical research and misinformation. Imperfections and major GM technology can also be combated by stricter regulation, monitoring and implementation by government agriculture bodies, a globally improved risk mitigation strategy and communication with growers, therefore ensuring greater acceptance. With key innovation in precision gene-integration technologies and emerging research in biofortification and stress tolerance, GM crops are forecast to bring productivity and profitability in commercial agriculture for smoother progress in the future.

ACKNOWLEDGMENTS

Although this review article is my own work, it would not have been possible without certain people. I would like to thank the editor and the reviewers for their helpful comments and remarks. I would also like to extend my gratitude towards the University of Melbourne staff, especially Dr. Matthew Digby and Mrs. Fiona Simpson for their encouragement in this venture. I would further extend my thanks to my peers, teachers and other people I met during my academic journey. Lastly, I would like to extend my deepest appreciation towards my family, who encouraged me to pursue a scientific career in Biotechnology and have been wonderfully supportive of my career goals. This review article is my maiden article in an academic journal, and I would like to thank all the readers for being a part of it.

Resistance and genetic divergence of wild cotton genotypes under attack by sucking pests

• Published: 24 September 2024
• Volume 220 , article number  157 , ( 2024 )

Cite this article

• Carlos Eduardo da Silva Oliveira   ORCID: orcid.org/0000-0002-3894-9559 1 ,
• Travis Wilson Witt   ORCID: orcid.org/0000-0003-4501-4230 2 ,
• Arshad Jalal   ORCID: orcid.org/0000-0002-2829-6996 3 , 7 ,
• Lúcia Vieira Hoffmann   ORCID: orcid.org/0000-0003-2150-1990 4 ,
• Gabriel da Silva Leite 3 ,
• Sebastião Soares de Oliveira Neto   ORCID: orcid.org/0000-0002-6372-4052 5 &
• Tiago Zoz   ORCID: orcid.org/0000-0003-2991-5485 6  

Sucking pests in recent years have become a concern for cotton cultivation worldwide. In this sense, the study aimed to identify cotton genotypes resistant to sucking insect attacks. The experiment was conducted in Cassilândia in the 2016/2017 harvest, using thirteen cotton genotypes. During the crop season, weekly samples of cotton aphid, Silverleaf whitefly, and cotton stainer were obtained. The data obtained were subjected to the analysis of variance and the Scott-Knott clustering algorithm at the level of 5% probability. Cotton genotypes G. thuberi , Gba04 , PAYM, G. trilobum , Gba01 , Gba02 , and Gba0 3 had the lowest populations of adults and nymphs of Silverleaf whitefly in the period. The genotypes PAYM , TX25, TX16, TX19, GBA03, IAC19, CO417, and EMPI were the most preferred for feeding by the cotton aphid. The cotton genotype IAC19 was the least preferred for feeding by cotton stainer; conversely, PAYM, Gba01 , and TX25 were the most preferred for feeding by these insects. G. thuberi , Gba04 , PAYM, G. trilobum , Gba01 , and Gba02 were less harmed by the attack of Silverleaf whitefly adults and nymphs; however, Gba01 , Gba02 , and Gba04 were the least preferred for feeding by cotton aphids, and IAC19 was the least preferred for feeding by cotton stainer. The cotton aphids and Silverleaf whitefly adults and/or nymphs have a high negative correlation with cotton yield, making the pests more harmful to the harvest.

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Acknowledgements

“Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. The USDA is an equal opportunity provider and employer”.

To the researcher Juliano Gomes Pádua, curator gene bank EMBRAPA (Brasília—DF), for partnership with the Mato Grosso do Sul State University, Crop Science Department for Cassilândia and for the wild cotton seeds provided by "Embrapa Genetic Resources and Biotechnology".

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Carlos Eduardo da Silva Oliveira

Department of Agriculture, Agricultural Research Service Grazinglands Research Laboratory, USDA-ARS, 7207 West Cheyenne Street, El Reno, OK, USA

Travis Wilson Witt

Department of Plant Protection, Rural Engineering, and Soils, School of Engineering, São Paulo State University, UNESP-FEIS, Ilha Solteira, SP, Brazil

Arshad Jalal & Gabriel da Silva Leite

Department of Crop Science, Brazilian Agricultural Research Agency – EMBRAPA, Santo Antônio de Goiás, Goiás, Brazil

Lúcia Vieira Hoffmann

Department of Crop Science, School of Agricultural Science, São Paulo State University, Botucatu, SP, Brazil

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Center for Studies and Innovations in Carbon Sequestration (CEISCO), Mato Grosso Do Sul State University – UEMS, Mundo Novo, MS, Brazil

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CESO, TZ, LVH, SSON, and TWW conceived research. CESO, AJ, GSL, and TZ conducted experiments. LVH, AJ, TWW, and TZ contributed material. CESO, GSL, and TZ analyzed data and conducted statistical analyses. CESO, TZ, AJ, and TWW wrote the manuscript. LVH, TZ, SSON, and TWW secured funding. All authors read and approved the manuscript.

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Oliveira, C.E., Witt, T.W., Jalal, A. et al. Resistance and genetic divergence of wild cotton genotypes under attack by sucking pests. Euphytica 220 , 157 (2024). https://doi.org/10.1007/s10681-024-03416-0

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Received : 24 October 2021

Accepted : 12 September 2024

Published : 24 September 2024

DOI : https://doi.org/10.1007/s10681-024-03416-0

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